initial commit

Minigrid2PRISM/.gitignore

@@ -0,0 +1,34 @@
+# Prerequisites
+*.d
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+
+build/*

Minigrid2PRISM/0001-WIP-differentiate-between-slippery-tilt-direction.patch

@@ -0,0 +1,125 @@
+From cbcb339cad57db456fb11dc844c99119cb8485e8 Mon Sep 17 00:00:00 2001
+From: sp <stefan.pranger@iaik.tugraz.at>
+Date: Wed, 5 Jul 2023 15:44:04 +0200
+Subject: [PATCH] WIP: differentiate between slippery tilt direction
+
+---
+ Minigrid2PRISM/util/Grid.cpp                | 24 +++++++++++++++------
+ Minigrid2PRISM/util/Grid.h                  |  5 ++++-
+ Minigrid2PRISM/util/MinigridGrammar.h       |  5 ++++-
+ Minigrid2PRISM/util/PrismModulesPrinter.cpp | 10 ++++-----
+ 4 files changed, 31 insertions(+), 13 deletions(-)
+
+diff --git a/Minigrid2PRISM/util/Grid.cpp b/Minigrid2PRISM/util/Grid.cpp
+index 9da5786..c6cf225 100644
+--- a/Minigrid2PRISM/util/Grid.cpp
++++ b/Minigrid2PRISM/util/Grid.cpp
+@@ -16,8 +16,17 @@ Grid::Grid(cells gridCells, cells background, const GridOptions &gridOptions, co
+   std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(floor), [](cell c) {
+       return c.type == Type::Floor;
+   });
+-  std::copy_if(background.begin(), background.end(), std::back_inserter(slippery), [](cell c) {
+-      return c.type == Type::Slippery;
++  std::copy_if(background.begin(), background.end(), std::back_inserter(northSlippery), [](cell c) {
++      return c.type == Type::NorthSlippery;
++  });
++  std::copy_if(background.begin(), background.end(), std::back_inserter(southSlippery), [](cell c) {
++      return c.type == Type::SouthSlippery;
++  });
++  std::copy_if(background.begin(), background.end(), std::back_inserter(eastSlippery), [](cell c) {
++      return c.type == Type::EastSlippery;
++  });
++  std::copy_if(background.begin(), background.end(), std::back_inserter(westSlippery), [](cell c) {
++      return c.type == Type::WestSlippery;
+   });
+   std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(lockedDoors), [](cell c) {
+       return c.type == Type::LockedDoor;
+@@ -158,11 +167,14 @@ void Grid::printToPrism(std::ostream& os, const prism::ModelType& modelType) {
+     std::set<std::string> slipperyActions;
+     if(agentWithProbabilisticBehaviour) printer.printModule(os, agentName, agentIndex, maxBoundaries, agentNameAndPosition->second, keys, agentWithView, gridOptions.probabilitiesForActions);
+     else                                printer.printModule(os, agentName, agentIndex, maxBoundaries, agentNameAndPosition->second, keys, agentWithView);
+-    for(auto const& c : slippery) {
++    for(auto const& c : northSlippery) {
++      printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 3);
++    for(auto const& c : southSlippery) {
++      printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 3);
++    for(auto const& c : eastSlippery) {
++      printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 3);
++    for(auto const& c : westSlippery) {
+       printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 3);
+-      printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 0);
+-      printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 1);
+-      printer.printSlippery(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), 2);
+       // printer.printSlipperyMoveNorth(os, agentName, agentIndex, c.getCoordinates(), isBlocked(c.getNorth()), isBlocked(c.getNorth(allGridCells).getEast()), isBlocked(c.getNorth(allGridCells).getWest()), slipperyActions, getWalkableDirOf8Neighborhood(c));
+       // printer.printSlipperyMoveEast( os, agentName, agentIndex, c.getCoordinates(), isBlocked(c.getEast()),  isBlocked(c.getNorth(allGridCells).getEast()), isBlocked(c.getSouth(allGridCells).getEast()), slipperyActions);
+       // printer.printSlipperyMoveSouth(os, agentName, agentIndex, c.getCoordinates(), isBlocked(c.getSouth()), isBlocked(c.getSouth(allGridCells).getEast()), isBlocked(c.getSouth(allGridCells).getWest()), slipperyActions);
+diff --git a/Minigrid2PRISM/util/Grid.h b/Minigrid2PRISM/util/Grid.h
+index f42b6e7..f908138 100644
+--- a/Minigrid2PRISM/util/Grid.h
++++ b/Minigrid2PRISM/util/Grid.h
+@@ -45,7 +45,10 @@ class Grid {
+ 
+     cells walls;
+     cells floor;
+-    cells slippery;
++    cells northSlippery;
++    cells southSlippery;
++    cells eastSlippery;
++    cells westSlippery;
+     cells lockedDoors;
+     cells boxes;
+     cells lava;
+diff --git a/Minigrid2PRISM/util/MinigridGrammar.h b/Minigrid2PRISM/util/MinigridGrammar.h
+index 5d73d87..e32db99 100644
+--- a/Minigrid2PRISM/util/MinigridGrammar.h
++++ b/Minigrid2PRISM/util/MinigridGrammar.h
+@@ -62,7 +62,10 @@ template <typename It>
+       ("B", Type::Box)
+       ("G", Type::Goal)
+       ("V", Type::Lava)
+-      ("S", Type::Slippery)
++      ("n", Type::NorthSlippery)
++      ("s", Type::SouthSlippery)
++      ("e", Type::EastSlippery)
++      ("w", Type::WestSlippery)
+       ("X", Type::Agent)
+       ("Z", Type::Adversary);
+     color_.add
+diff --git a/Minigrid2PRISM/util/PrismModulesPrinter.cpp b/Minigrid2PRISM/util/PrismModulesPrinter.cpp
+index b6661ef..3a32932 100644
+--- a/Minigrid2PRISM/util/PrismModulesPrinter.cpp
++++ b/Minigrid2PRISM/util/PrismModulesPrinter.cpp
+@@ -184,9 +184,9 @@ namespace prism {
+     if(agentWithView) {
+       os << "\tview" << agentName << " : [0..3] init 0;\n";
+     os << "\n";
+-    os << "\t[" << agentName << "_turn_right] " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava -> (view" << agentName << "'=mod(view" << agentName << " + 1, 4)) " << moveUpdate(agentIndex) << ";\n";
+-    os << "\t[" << agentName << "_turn_left]  " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & view" << agentName << ">0 -> (view" << agentName << "'=view" << agentName << " - 1) " << moveUpdate(agentIndex) << ";\n";
+-    os << "\t[" << agentName << "_turn_left]  " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & view" << agentName << "=0 -> (view" << agentName << "'=3) " << moveUpdate(agentIndex) << ";\n";
++    os << "\t[" << agentName << "_turn_right] " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & !AgentIsOnSlippery-> (view" << agentName << "'=mod(view" << agentName << " + 1, 4)) " << moveUpdate(agentIndex) << ";\n";
++    os << "\t[" << agentName << "_turn_left]  " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & !AgentIsOnSlippery& view" << agentName << ">0 -> (view" << agentName << "'=view" << agentName << " - 1) " << moveUpdate(agentIndex) << ";\n";
++    os << "\t[" << agentName << "_turn_left]  " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & !AgentIsOnSlippery& view" << agentName << "=0 -> (view" << agentName << "'=3) " << moveUpdate(agentIndex) << ";\n";
+     } else {
+       os << "\t[" << agentName << "_turns]  " << moveGuard(agentIndex) << " true -> (x" << agentName << "'=x" << agentName << ")" << moveUpdate(agentIndex) << ";\n";
+     }
+@@ -256,7 +256,7 @@ namespace prism {
+     // direction specifics
+ 
+     std::size_t straightPosIndex;
+-    std::string actionName, specialTransition; // if straight ahead is blocked 
++    std::string actionName, specialTransition; // if straight ahead is blocked
+     std::array<std::size_t, ALL_POSS_DIRECTIONS> prob_piece_dir; // from north clockwise
+ 
+     switch (direction)
+@@ -367,7 +367,7 @@ namespace prism {
+ 
+   std::ostream& PrismModulesPrinter::printRewards(std::ostream &os, const std::map<coordinates, float> &stateRewards, const cells &lava, const cells &goals) {
+     os << "rewards \"NoBFS\"\n";
+-    os << "\tAgentIsInGoalAndNotDone:  100;\n";
++    //os << "\tAgentIsInGoalAndNotDone:  100;\n";
+     os << "\tAgentIsInLavaAndNotDone: -100;\n";
+     os << "endrewards\n";
+     os << "rewards \"WithBFS\"\n";
+-- 
+2.25.1
+

Minigrid2PRISM/CMakeLists.txt

@@ -0,0 +1,22 @@
+include(util/CMakeLists.txt)
+
+set(CMAKE_CXX_STANDARD 20)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__FILENAME__='\"$(subst ${CMAKE_SOURCE_DIR}/,,$(abspath $<))\"'")
+add_definitions(-DLOG_DEBUG)
+
+cmake_minimum_required(VERSION 3.0...3.22)
+
+set(CMAKE_BUILD_TYPE Debug)
+
+project(
+  Minigrid2PRISM
+  VERSION 0.1
+  LANGUAGES CXX)
+
+add_executable(main
+               ${SRCS}
+               main.cpp
+               )
+
+target_link_libraries(main pthread)

Minigrid2PRISM/agentView.prism

@@ -0,0 +1,92 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_turns]
+endplayer
+player Green
+	[Green_move_north], [Green_move_east], [Green_move_south], [Green_move_west], [Green_turns]
+endplayer
+
+global move : [0..2] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label GreenOnBlue = (xGreen=1&yGreen=1) | (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=3&yGreen=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label GreenOnGreen = (xGreen=9&yGreen=1) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+label GreenOnRed = (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+formula GreenCannotMoveNorth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=1&yGreen=1);
+formula GreenCannotMoveEast  = (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=11) | (xGreen=4&yGreen=11) | (xGreen=5&yGreen=11) | (xGreen=6&yGreen=11) | (xGreen=8&yGreen=11) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=11) | (xGreen=7&yGreen=11);
+formula GreenCannotMoveSouth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+formula GreenCannotMoveWest  = (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=5&yGreen=1) | (xGreen=6&yGreen=1) | (xGreen=7&yGreen=1) | (xGreen=8&yGreen=1) | (xGreen=9&yGreen=1) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=1&yGreen=1) | (xGreen=4&yGreen=1);
+formula GreenIsOnSlippery = false;
+
+label GreenGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right]  move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	[Blue_turns]   move=1 &  true -> (xBlue'=xBlue) & (move'=2) ;
+
+
+	[Blue_move_north] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=2) ;
+	[Blue_move_east]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=2) ;
+	[Blue_move_south] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=2) ;
+	[Blue_move_west]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=2) ;
+
+endmodule
+
+module Green
+	xGreen : [1..12] init 4;
+	yGreen : [1..12] init 1;
+
+	[Green_turns]   move=2 &  true -> (xGreen'=xGreen) & (move'=0) ;
+
+
+	[Green_move_north] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveNorth -> (xGreen'=xGreen-1) & (move'=0) ;
+	[Green_move_east]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveEast  -> (yGreen'=yGreen+1) & (move'=0) ;
+	[Green_move_south] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveSouth -> (xGreen'=xGreen+1) & (move'=0) ;
+	[Green_move_west]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveWest  -> (yGreen'=yGreen-1) & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/agentViewOnlyBlue.prism

@@ -0,0 +1,65 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_turns]
+endplayer
+
+global move : [0..1] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue);
+
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right]  move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	[Blue_turns]   move=1 &  true -> (xBlue'=xBlue) & (move'=0) ;
+
+
+	[Blue_move_north] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=0) ;
+	[Blue_move_east]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=0) ;
+	[Blue_move_south] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=0) ;
+	[Blue_move_west]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/allView.prism

@@ -0,0 +1,100 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_turn_left], [Blue_turn_right]
+endplayer
+player Green
+	[Green_move_north], [Green_move_east], [Green_move_south], [Green_move_west], [Green_turn_left], [Green_turn_right]
+endplayer
+
+global move : [0..2] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label GreenOnBlue = (xGreen=1&yGreen=1) | (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=3&yGreen=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label GreenOnGreen = (xGreen=9&yGreen=1) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+label GreenOnRed = (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+formula GreenCannotMoveNorth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=1&yGreen=1);
+formula GreenCannotMoveEast  = (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=11) | (xGreen=4&yGreen=11) | (xGreen=5&yGreen=11) | (xGreen=6&yGreen=11) | (xGreen=8&yGreen=11) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=11) | (xGreen=7&yGreen=11);
+formula GreenCannotMoveSouth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+formula GreenCannotMoveWest  = (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=5&yGreen=1) | (xGreen=6&yGreen=1) | (xGreen=7&yGreen=1) | (xGreen=8&yGreen=1) | (xGreen=9&yGreen=1) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=1&yGreen=1) | (xGreen=4&yGreen=1);
+formula GreenIsOnSlippery = false;
+
+label GreenGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right]  move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	viewBlue : [0..3] init 1;
+
+	[Blue_turn_right]  move=1 &  true -> (viewBlue'=mod(viewBlue + 1, 4))  & (move'=2) ;
+	[Blue_turn_left]   move=1 &  viewBlue>0 -> (viewBlue'=viewBlue - 1)  & (move'=2) ;
+	[Blue_turn_left]   move=1 &  viewBlue=0 -> (viewBlue'=3)  & (move'=2) ;
+
+
+	[Blue_move_north] move=1 &  viewBlue=3 &  !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=2) ;
+	[Blue_move_east]  move=1 &  viewBlue=0 &  !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=2) ;
+	[Blue_move_south] move=1 &  viewBlue=1 &  !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=2) ;
+	[Blue_move_west]  move=1 &  viewBlue=2 &  !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=2) ;
+
+endmodule
+
+module Green
+	xGreen : [1..12] init 4;
+	yGreen : [1..12] init 1;
+
+	viewGreen : [0..3] init 1;
+
+	[Green_turn_right]  move=2 &  true -> (viewGreen'=mod(viewGreen + 1, 4))  & (move'=0) ;
+	[Green_turn_left]   move=2 &  viewGreen>0 -> (viewGreen'=viewGreen - 1)  & (move'=0) ;
+	[Green_turn_left]   move=2 &  viewGreen=0 -> (viewGreen'=3)  & (move'=0) ;
+
+
+	[Green_move_north] move=2 &  viewGreen=3 &  !GreenIsOnSlippery & !GreenCannotMoveNorth -> (xGreen'=xGreen-1) & (move'=0) ;
+	[Green_move_east]  move=2 &  viewGreen=0 &  !GreenIsOnSlippery & !GreenCannotMoveEast  -> (yGreen'=yGreen+1) & (move'=0) ;
+	[Green_move_south] move=2 &  viewGreen=1 &  !GreenIsOnSlippery & !GreenCannotMoveSouth -> (xGreen'=xGreen+1) & (move'=0) ;
+	[Green_move_west]  move=2 &  viewGreen=2 &  !GreenIsOnSlippery & !GreenCannotMoveWest  -> (yGreen'=yGreen-1) & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/both.prism

@@ -0,0 +1,100 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_move_north_10], [Blue_move_east_10], [Blue_move_south_10], [Blue_move_west_10], [Blue_turns]
+endplayer
+player Green
+	[Green_move_north], [Green_move_east], [Green_move_south], [Green_move_west], [Green_move_north_10], [Green_move_east_10], [Green_move_south_10], [Green_move_west_10], [Green_turns]
+endplayer
+
+global move : [0..2] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label GreenOnBlue = (xGreen=1&yGreen=1) | (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=3&yGreen=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label GreenOnGreen = (xGreen=9&yGreen=1) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+label GreenOnRed = (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+formula GreenCannotMoveNorth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=1&yGreen=1);
+formula GreenCannotMoveEast  = (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=11) | (xGreen=4&yGreen=11) | (xGreen=5&yGreen=11) | (xGreen=6&yGreen=11) | (xGreen=8&yGreen=11) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=11) | (xGreen=7&yGreen=11);
+formula GreenCannotMoveSouth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+formula GreenCannotMoveWest  = (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=5&yGreen=1) | (xGreen=6&yGreen=1) | (xGreen=7&yGreen=1) | (xGreen=8&yGreen=1) | (xGreen=9&yGreen=1) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=1&yGreen=1) | (xGreen=4&yGreen=1);
+formula GreenIsOnSlippery = false;
+
+label GreenGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right]  move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	[Blue_turns]   move=1 &  true -> (xBlue'=xBlue) & (move'=2) ;
+
+
+	[Blue_move_north] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=2) ;
+	[Blue_move_east]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=2) ;
+	[Blue_move_south] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=2) ;
+	[Blue_move_west]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=2) ;
+	[Blue_move_north_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> 0.100000: (xBlue'=xBlue-1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_east_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> 0.100000: (yBlue'=yBlue+1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_south_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> 0.100000: (xBlue'=xBlue+1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_west_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> 0.100000: (yBlue'=yBlue-1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+
+endmodule
+
+module Green
+	xGreen : [1..12] init 4;
+	yGreen : [1..12] init 1;
+
+	[Green_turns]   move=2 &  true -> (xGreen'=xGreen) & (move'=0) ;
+
+
+	[Green_move_north] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveNorth -> (xGreen'=xGreen-1) & (move'=0) ;
+	[Green_move_east]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveEast  -> (yGreen'=yGreen+1) & (move'=0) ;
+	[Green_move_south] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveSouth -> (xGreen'=xGreen+1) & (move'=0) ;
+	[Green_move_west]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveWest  -> (yGreen'=yGreen-1) & (move'=0) ;
+	[Green_move_north_10]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveNorth -> 0.100000: (xGreen'=xGreen-1) & (move'=0)  + 0.900000: (xGreen'=xGreen)  & (move'=0) ;
+	[Green_move_east_10]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveEast  -> 0.100000: (yGreen'=yGreen+1) & (move'=0)  + 0.900000: (xGreen'=xGreen)  & (move'=0) ;
+	[Green_move_south_10]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveSouth -> 0.100000: (xGreen'=xGreen+1) & (move'=0)  + 0.900000: (xGreen'=xGreen)  & (move'=0) ;
+	[Green_move_west_10]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveWest  -> 0.100000: (yGreen'=yGreen-1) & (move'=0)  + 0.900000: (xGreen'=xGreen)  & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/emptyGrid

@@ -0,0 +1,13 @@
+WGWGWGWGWGWGWGWGWGWGWGWGWG
+WGXR                    WG
+WG                      WG
+WG                      WG
+WG                    ZGWG
+WG                      WG
+WG                      WG
+WG                      WG
+WG                      WG
+WG                      WG
+WG                      WG
+WG                    ZBWG
+WGWGWGWGWGWGWGWGWGWGWGWGWG

Minigrid2PRISM/foobar

@@ -0,0 +1,96 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turns]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_move_north_10], [Blue_move_east_10], [Blue_move_south_10], [Blue_move_west_10], [Blue_move_north_21], [Blue_move_east_21], [Blue_move_south_21], [Blue_move_west_21], [Blue_turns]
+endplayer
+player Green
+	[Green_move_north], [Green_move_east], [Green_move_south], [Green_move_west], [Green_turns]
+endplayer
+
+global move : [0..2] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label GreenOnBlue = (xGreen=1&yGreen=1) | (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=3&yGreen=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label GreenOnGreen = (xGreen=9&yGreen=1) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+label GreenOnRed = (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+formula GreenCannotMoveNorth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=1&yGreen=1);
+formula GreenCannotMoveEast  = (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=11) | (xGreen=4&yGreen=11) | (xGreen=5&yGreen=11) | (xGreen=6&yGreen=11) | (xGreen=8&yGreen=11) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=11) | (xGreen=7&yGreen=11);
+formula GreenCannotMoveSouth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+formula GreenCannotMoveWest  = (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=5&yGreen=1) | (xGreen=6&yGreen=1) | (xGreen=7&yGreen=1) | (xGreen=8&yGreen=1) | (xGreen=9&yGreen=1) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=1&yGreen=1) | (xGreen=4&yGreen=1);
+formula GreenIsOnSlippery = false;
+
+label GreenGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	[Agent_turns]   move=0 &  true -> (xAgent'=xAgent) & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &   !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &   !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &   !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &   !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	[Blue_turns]   move=1 &  true -> (xBlue'=xBlue) & (move'=2) ;
+
+
+	[Blue_move_north] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=2) ;
+	[Blue_move_east]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=2) ;
+	[Blue_move_south] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=2) ;
+	[Blue_move_west]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=2) ;
+	[Blue_move_north_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> 0.100000: (xBlue'=xBlue-1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_east_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> 0.100000: (yBlue'=yBlue+1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_south_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> 0.100000: (xBlue'=xBlue+1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_west_10]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> 0.100000: (yBlue'=yBlue-1) & (move'=2)  + 0.900000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_north_21]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> 0.210000: (xBlue'=xBlue-1) & (move'=2)  + 0.790000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_east_21]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> 0.210000: (yBlue'=yBlue+1) & (move'=2)  + 0.790000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_south_21]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> 0.210000: (xBlue'=xBlue+1) & (move'=2)  + 0.790000: (xBlue'=xBlue)  & (move'=2) ;
+	[Blue_move_west_21]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> 0.210000: (yBlue'=yBlue-1) & (move'=2)  + 0.790000: (xBlue'=xBlue)  & (move'=2) ;
+
+endmodule
+
+module Green
+	xGreen : [1..12] init 4;
+	yGreen : [1..12] init 1;
+
+	[Green_turns]   move=2 &  true -> (xGreen'=xGreen) & (move'=0) ;
+
+
+	[Green_move_north] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveNorth -> (xGreen'=xGreen-1) & (move'=0) ;
+	[Green_move_east]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveEast  -> (yGreen'=yGreen+1) & (move'=0) ;
+	[Green_move_south] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveSouth -> (xGreen'=xGreen+1) & (move'=0) ;
+	[Green_move_west]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveWest  -> (yGreen'=yGreen-1) & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/grid

@@ -0,0 +1,27 @@
+WGWGWGWGWGWGWGWGWGWGWGWGWG
+WGXR                    WG
+WG  WGWGWGWGWGWGWGWGWG  WG
+WG                    SBWG
+WGZGSB  SBSB  SB  SB    WG
+WG                  SB  WG
+WG      SB              WG
+WG    SB    SB    SB  ZBWG
+WG  SB                  WG
+WG      SB    SB    SB  WG
+WG  WGWGWGWGWGWGWGWGWG  WG
+WG                    GRWG
+WGWGWGWGWGWGWGWGWGWGWGWGWG
+--------------------------
+WGWGWGWGWGWGWGWGWGWGWGWGWG
+WG B B B R R B B B B B BWG
+WG BWGWGWGWGWGWGWGWGWG BWG
+WG B B B B B B B B B B BWG
+WG                      WG
+WG                      WG
+WG                      WG
+WG                      WG
+WG                      WG
+WG G G G G G G G G G G GWG
+WG GWGWGWGWGWGWGWGWGWG GWG
+WG G G G G G G G G G G GWG
+WGWGWGWGWGWGWGWGWGWGWGWGWG

Minigrid2PRISM/grid.prism

@@ -0,0 +1,65 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_turns]
+endplayer
+
+global move : [0..1] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=5);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=5);
+label AgentOnGreen = (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=4&yAgent=1) | (xAgent=4&yAgent=5) | (xAgent=5&yAgent=1) | (xAgent=5&yAgent=2) | (xAgent=5&yAgent=3) | (xAgent=5&yAgent=4) | (xAgent=5&yAgent=5);
+label BlueOnGreen = (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=4&yBlue=1) | (xBlue=4&yBlue=5) | (xBlue=5&yBlue=1) | (xBlue=5&yBlue=2) | (xBlue=5&yBlue=3) | (xBlue=5&yBlue=4) | (xBlue=5&yBlue=5);
+label AgentOnRed = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3);
+label BlueOnRed = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=4) | (xAgent=5&yAgent=2) | (xAgent=5&yAgent=4) | (xAgent=3&yAgent=3) | (xAgent=5&yAgent=3) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=5) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=5) | (xAgent=3&yAgent=5) | (xAgent=4&yAgent=5) | (xAgent=4&yAgent=1) | (xAgent=5&yAgent=5);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=4) | (xAgent=5&yAgent=1) | (xAgent=5&yAgent=2) | (xAgent=5&yAgent=4) | (xAgent=3&yAgent=3) | (xAgent=5&yAgent=3) | (xAgent=5&yAgent=5);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=5) | (xAgent=3&yAgent=1) | (xAgent=4&yAgent=5) | (xAgent=5&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=4) | (xBlue=5&yBlue=2) | (xBlue=5&yBlue=4) | (xBlue=3&yBlue=3) | (xBlue=5&yBlue=3) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=5) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=5) | (xBlue=3&yBlue=5) | (xBlue=4&yBlue=5) | (xBlue=4&yBlue=1) | (xBlue=5&yBlue=5);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=4) | (xBlue=5&yBlue=1) | (xBlue=5&yBlue=2) | (xBlue=5&yBlue=4) | (xBlue=3&yBlue=3) | (xBlue=5&yBlue=3) | (xBlue=5&yBlue=5);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=5) | (xBlue=3&yBlue=1) | (xBlue=4&yBlue=5) | (xBlue=5&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue);
+
+module Agent
+	xAgent : [1..6] init 1;
+	yAgent : [1..6] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right]  move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=1) ;
+	[Agent_turn_left]   move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..6] init 5;
+	yBlue : [1..6] init 5;
+
+	[Blue_turns]   move=1 &  true -> (xBlue'=xBlue) & (move'=0) ;
+
+
+	[Blue_move_north] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=0) ;
+	[Blue_move_east]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=0) ;
+	[Blue_move_south] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=0) ;
+	[Blue_move_west]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/gridLava

@@ -0,0 +1,19 @@
+WGWGWGWGWGWGWGWGWG
+WG              WG
+WG  LR          WG
+WG  LR          WG
+WG  LR          WG
+WG  LR          WG
+WG  LR          WG
+WGXRLR        GGWG
+WGWGWGWGWGWGWGWGWG
+------------------
+WGWGWGWGWGWGWGWGWG
+WG              WG
+WG              WG
+WG              WG
+WG              WG
+WG              WG
+WG              WG
+WG              WG
+WGWGWGWGWGWGWGWGWG

Minigrid2PRISM/gridLava.prism

@@ -0,0 +1,35 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right]
+endplayer
+
+global move : [0..0] init 0;
+
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=7) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=7) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=7) | (xAgent=4&yAgent=1) | (xAgent=4&yAgent=7) | (xAgent=5&yAgent=1) | (xAgent=5&yAgent=7) | (xAgent=6&yAgent=1) | (xAgent=6&yAgent=7) | (xAgent=7&yAgent=7) | (xAgent=7&yAgent=1);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=7&yAgent=3) | (xAgent=7&yAgent=4) | (xAgent=7&yAgent=5) | (xAgent=7&yAgent=6) | (xAgent=7&yAgent=7) | (xAgent=7&yAgent=1);
+formula AgentCannotMoveWest  = (xAgent=1&yAgent=1) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=3) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=3) | (xAgent=4&yAgent=1) | (xAgent=4&yAgent=3) | (xAgent=5&yAgent=1) | (xAgent=5&yAgent=3) | (xAgent=6&yAgent=1) | (xAgent=6&yAgent=3) | (xAgent=7&yAgent=3) | (xAgent=7&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = (xAgent=7&yAgent=7);
+label crash = ;
+
+module Agent
+	xAgent : [1..8] init 7;
+	yAgent : [1..8] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right]  move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=0) ;
+	[Agent_turn_left]   move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=0) ;
+	[Agent_turn_left]   move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=0) ;
+
+
+	[Agent_move_north] move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=0) ;
+	[Agent_move_east]  move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=0) ;
+	[Agent_move_south] move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=0) ;
+	[Agent_move_west]  move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=0) ;
+
+endmodule
+

Minigrid2PRISM/gridSave

@@ -0,0 +1,15 @@
+WGWGWGWGWGWGWG
+WGXR        WG
+WG  WGWGWG  WG
+WG    BB    WG
+WGZGWGWGWG  WG
+WG    BR  ZBWG
+WGWGWGWGWGWGWG
+--------------
+WGWGWGWGWGWGWG
+WG B R R B BWG
+WG BWGWGWG BWG
+WG G G G G GWG
+WG GWGWGWG GWG
+WG G G G G GWG
+WGWGWGWGWGWGWG

Minigrid2PRISM/main.cpp

@@ -0,0 +1,166 @@
+#include "util/OptionParser.h"
+#include "util/MinigridGrammar.h"
+#include "util/Grid.h"
+
+#include <iostream>
+#include <fstream>
+#include <filesystem>
+#include <sstream>
+
+std::vector<std::string> parseCommaSeparatedString(std::string const& str) {
+  std::vector<std::string> result;
+  std::stringstream stream(str);
+  while(stream.good()) {
+    std::string substr;
+    getline(stream, substr, ',');
+    substr.at(0) = std::toupper(substr.at(0));
+    result.push_back(substr);
+  }
+  return result;
+}
+
+struct printer {
+    typedef boost::spirit::utf8_string string;
+
+    void element(string const& tag, string const& value, int depth) const {
+        for (int i = 0; i < (depth*4); ++i) std::cout << ' ';
+
+        std::cout << "tag: " << tag;
+        if (value != "") std::cout << ", value: " << value;
+        std::cout << std::endl;
+    }
+};
+
+void print_info(boost::spirit::info const& what) {
+  using boost::spirit::basic_info_walker;
+
+  printer pr;
+  basic_info_walker<printer> walker(pr, what.tag, 0);
+  boost::apply_visitor(walker, what.value);
+}
+
+int main(int argc, char* argv[]) {
+  popl::OptionParser optionParser("Allowed options");
+
+  auto helpOption = optionParser.add<popl::Switch>("h", "help", "Print this help message.");
+  auto inputFilename = optionParser.add<popl::Value<std::string>>("i", "input-file", "Filename of the input file.");
+  auto outputFilename = optionParser.add<popl::Value<std::string>>("o", "output-file", "Filename for the output file.");
+
+  auto agentsToBeConsidered = optionParser.add<popl::Value<std::string>, popl::Attribute::optional>("a", "agents", "Which parsed agents should be considered in the output. WIP.");
+  auto viewForAgents = optionParser.add<popl::Value<std::string>, popl::Attribute::optional>("v", "view", "Agents for which the 'view'('direction') variable should be included. WIP.");
+
+  auto probabilisticBehaviourAgents = optionParser.add<popl::Value<std::string>, popl::Attribute::optional>("p", "prob-beh", "Agents for which we want to include probabilistic actions");
+  auto probabilities = optionParser.add<popl::Value<std::string>, popl::Attribute::optional>("q", "probs", "The probabilities for which probabilistic actions should be added. WIP");
+
+  try {
+    optionParser.parse(argc, argv);
+
+    if(helpOption->count() > 0) {
+      std::cout << optionParser << std::endl;
+      return EXIT_SUCCESS;
+    }
+  } catch (const popl::invalid_option &e) {
+    return io::printPoplException(e);
+  } catch (const std::exception &e) {
+		std::cerr << "Exception: " << e.what() << "\n";
+		return EXIT_FAILURE;
+	}
+
+  GridOptions gridOptions = { {}, {} };
+  if(agentsToBeConsidered->is_set()) {
+    gridOptions.agentsToBeConsidered = parseCommaSeparatedString(agentsToBeConsidered->value(0));
+  }
+  if(viewForAgents->is_set()) {
+    gridOptions.agentsWithView = parseCommaSeparatedString(viewForAgents->value(0));
+  }
+  if(probabilisticBehaviourAgents->is_set()) {
+    gridOptions.agentsWithProbabilisticBehaviour = parseCommaSeparatedString(probabilisticBehaviourAgents->value(0));
+    for(auto const& a : gridOptions.agentsWithProbabilisticBehaviour) {
+      std::cout << a << std::endl;
+    }
+    if(probabilities->is_set()) {
+      std::vector<std::string> parsedStrings = parseCommaSeparatedString(probabilities->value(0));
+
+      std::transform(parsedStrings.begin(), parsedStrings.end(), std::back_inserter(gridOptions.probabilitiesForActions), [](const std::string& string) {
+        return std::stof(string);
+      });
+      for(auto const& a : gridOptions.probabilitiesForActions) {
+        std::cout << a << std::endl;
+      }
+    } else {
+      throw std::logic_error{ "When adding agents with probabilistic behaviour, you also need to specify a list of probabilities via --probs." };
+    }
+  }
+
+
+  std::fstream file {outputFilename->value(0), file.trunc | file.out};
+  std::fstream infile {inputFilename->value(0), infile.in};
+  std::string line, content, background, rewards;
+  std::cout << "\n";
+  bool parsingBackground = false;
+  bool parsingStateRewards = false;
+  while (std::getline(infile, line) && !line.empty()) {
+    if(line.at(0) == '-' && line.at(line.size() - 1) == '-' && parsingBackground) {
+      parsingStateRewards = true;
+      parsingBackground = false;
+      continue;
+    } else if(line.at(0) == '-' && line.at(line.size() - 1) == '-') {
+      parsingBackground = true;
+      continue;
+    }
+    if(!parsingBackground && !parsingStateRewards) {
+      std::cout << "Reading   :\t" << line << "\n";
+      content += line + "\n";
+    } else if (parsingBackground) {
+      std::cout << "Background:\t" << line << "\n";
+      background += line + "\n";
+    } else if(parsingStateRewards) {
+      rewards += line + "\n";
+    }
+  }
+  std::cout << "\n";
+
+
+  pos_iterator_t contentFirst(content.begin());
+  pos_iterator_t contentIter = contentFirst;
+  pos_iterator_t contentLast(content.end());
+  MinigridParser<pos_iterator_t> contentParser(contentFirst);
+  pos_iterator_t backgroundFirst(background.begin());
+  pos_iterator_t backgroundIter = backgroundFirst;
+  pos_iterator_t backgroundLast(background.end());
+  MinigridParser<pos_iterator_t> backgroundParser(backgroundFirst);
+
+  cells contentCells;
+  cells backgroundCells;
+  std::map<coordinates, float> stateRewards;
+  try {
+    bool ok = phrase_parse(contentIter, contentLast, contentParser, qi::space, contentCells);
+    // TODO if(background is not empty) {
+    ok     &= phrase_parse(backgroundIter, backgroundLast, backgroundParser, qi::space, backgroundCells);
+    // TODO }
+
+    boost::escaped_list_separator<char> seps('\\', ';', '\n');
+    Tokenizer csvParser(rewards, seps);
+    for(auto iter = csvParser.begin(); iter != csvParser.end(); ++iter) {
+      int x = std::stoi(*iter);
+      int y = std::stoi(*(++iter));
+      float reward = std::stof(*(++iter));
+      stateRewards[std::make_pair(x,y)] = reward;
+    }
+    if(ok) {
+      Grid grid(contentCells, backgroundCells, gridOptions, stateRewards);
+      //grid.printToPrism(std::cout, prism::ModelType::MDP);
+      grid.printToPrism(file, prism::ModelType::MDP);
+    }
+  } catch(qi::expectation_failure<pos_iterator_t> const& e) {
+    std::cout << "expected: "; print_info(e.what_);
+    std::cout << "got: \"" << std::string(e.first, e.last) << '"' << std::endl;
+    std::cout << "Expectation failure: " << e.what() << " at '" << std::string(e.first,e.last) << "'\n";
+  } catch(const std::exception& e) {
+    std::cerr << "Exception '" << typeid(e).name() << "' caught:" << std::endl;
+    std::cerr << "\t" << e.what() << std::endl;
+    std::exit(EXIT_FAILURE);
+  }
+
+  return 0;
+}

Minigrid2PRISM/noview.prism

@@ -0,0 +1,88 @@
+smg
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turns]
+endplayer
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_turns]
+endplayer
+player Green
+	[Green_move_north], [Green_move_east], [Green_move_south], [Green_move_west], [Green_turns]
+endplayer
+
+global move : [0..2] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label GreenOnBlue = (xGreen=1&yGreen=1) | (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=3&yGreen=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label GreenOnGreen = (xGreen=9&yGreen=1) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+label GreenOnRed = (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = false;
+
+label AgentGoal = false;
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = false;
+
+label BlueGoal = false;
+formula GreenCannotMoveNorth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=1&yGreen=1);
+formula GreenCannotMoveEast  = (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=11) | (xGreen=4&yGreen=11) | (xGreen=5&yGreen=11) | (xGreen=6&yGreen=11) | (xGreen=8&yGreen=11) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=11) | (xGreen=7&yGreen=11);
+formula GreenCannotMoveSouth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+formula GreenCannotMoveWest  = (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=5&yGreen=1) | (xGreen=6&yGreen=1) | (xGreen=7&yGreen=1) | (xGreen=8&yGreen=1) | (xGreen=9&yGreen=1) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=1&yGreen=1) | (xGreen=4&yGreen=1);
+formula GreenIsOnSlippery = false;
+
+label GreenGoal = false;
+label crash = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	[Agent_turns]   move=0 &  true -> (xAgent'=xAgent) & (move'=1) ;
+
+
+	[Agent_move_north] move=0 &   !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  move=0 &   !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] move=0 &   !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  move=0 &   !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+endmodule
+
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	[Blue_turns]   move=1 &  true -> (xBlue'=xBlue) & (move'=2) ;
+
+
+	[Blue_move_north] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=2) ;
+	[Blue_move_east]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=2) ;
+	[Blue_move_south] move=1 &   !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=2) ;
+	[Blue_move_west]  move=1 &   !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=2) ;
+
+endmodule
+
+module Green
+	xGreen : [1..12] init 4;
+	yGreen : [1..12] init 1;
+
+	[Green_turns]   move=2 &  true -> (xGreen'=xGreen) & (move'=0) ;
+
+
+	[Green_move_north] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveNorth -> (xGreen'=xGreen-1) & (move'=0) ;
+	[Green_move_east]  move=2 &   !GreenIsOnSlippery & !GreenCannotMoveEast  -> (yGreen'=yGreen+1) & (move'=0) ;
+	[Green_move_south] move=2 &   !GreenIsOnSlippery & !GreenCannotMoveSouth -> (xGreen'=xGreen+1) & (move'=0) ;
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Minigrid2PRISM/property.prism

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+//<Test, PreSafety, lambda=0.89> <<one>> Pmax=? [G !"crash" ];

Minigrid2PRISM/smallerGrid

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+WGXR        WG
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+WGZG        WG
+WG  BBBG    WG
+WG    BR  ZBWG
+WG          WG
+WG  WGWGWG  WG
+WG          WG
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+!_TAG_PROGRAM_NAME	Exuberant Ctags	//
+!_TAG_PROGRAM_URL	http://ctags.sourceforge.net	/official site/
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+main	build/CMakeFiles/3.16.3/CompilerIdCXX/CMakeCXXCompilerId.cpp	/^int main(int argc, char* argv[])$/;"	f
+main	main.cpp	/^int main() {$/;"	f
+maxBoundaries	util/Grid.h	/^    coordinates maxBoundaries;$/;"	m	class:Grid
+missing_argument	util/popl.hpp	/^        missing_argument,$/;"	m	class:popl::Attribute::invalid_option::Error
+modelType	util/PrismModulesPrinter.h	/^      ModelType const& modelType;$/;"	m	class:prism::PrismModulesPrinter
+moveGuard	util/PrismModulesPrinter.cpp	/^  std::string PrismModulesPrinter::moveGuard(const size_t &agentIndex) {$/;"	f	class:prism::PrismModulesPrinter
+moveUpdate	util/PrismModulesPrinter.cpp	/^  std::string PrismModulesPrinter::moveUpdate(const size_t &agentIndex) {$/;"	f	class:prism::PrismModulesPrinter
+name	util/popl.hpp	/^inline std::string Option::name(OptionName what_name, bool with_hypen) const$/;"	f	class:popl::Attribute::Option
+no	util/popl.hpp	/^    no = 0,   \/\/ option never takes an argument$/;"	m	class:popl::Argument
+non_option_args	util/popl.hpp	/^inline const std::vector<std::string>& OptionParser::non_option_args() const$/;"	f	class:popl::Attribute::OptionParser
+non_option_args_	util/popl.hpp	/^    std::vector<std::string> non_option_args_;$/;"	m	class:popl::Attribute::OptionParser
+numberOfPlayer	util/PrismModulesPrinter.h	/^      const size_t numberOfPlayer;$/;"	m	class:prism::PrismModulesPrinter
+operator ()	util/MinigridGrammar.h	/^    void operator()(Val& v, First f, Last l) const {$/;"	f	struct:annotation_f
+operator <<	util/Grid.cpp	/^std::ostream& operator<<(std::ostream& os, const Grid& grid) {$/;"	f
+operator <<	util/cell.cpp	/^std::ostream &operator<<(std::ostream &os, const cell &c) {$/;"	f
+operator <<	util/popl.hpp	/^static inline std::ostream& operator<<(std::ostream& out, const OptionParser& op)$/;"	f	class:popl::Attribute
+option	util/popl.hpp	/^    const Option* option() const$/;"	f	class:popl::Attribute::invalid_option
+option_	util/popl.hpp	/^    const Option* option_;$/;"	m	class:popl::Attribute::invalid_option
+option_parser_	util/popl.hpp	/^    const OptionParser* option_parser_;$/;"	m	class:popl::Attribute::OptionPrinter
+optional	util/popl.hpp	/^    optional = 3,$/;"	m	class:popl::Attribute
+options	util/popl.hpp	/^inline const std::vector<Option_ptr>& OptionParser::options() const$/;"	f	class:popl::Attribute::OptionParser
+options_	util/popl.hpp	/^    std::vector<Option_ptr> options_;$/;"	m	class:popl::Attribute::OptionParser
+override	util/popl.hpp	/^    Argument argument_type() const override;$/;"	m	class:popl::Attribute::Implicit
+override	util/popl.hpp	/^    Argument argument_type() const override;$/;"	m	class:popl::Attribute::Switch
+override	util/popl.hpp	/^    Argument argument_type() const override;$/;"	m	class:popl::Attribute::Value
+override	util/popl.hpp	/^    bool get_default(std::ostream& out) const override;$/;"	m	class:popl::Attribute::Value
+override	util/popl.hpp	/^    bool is_set() const override;$/;"	m	class:popl::Attribute::Value
+override	util/popl.hpp	/^    size_t count() const override;$/;"	m	class:popl::Attribute::Value
+override	util/popl.hpp	/^    std::string print(const Attribute& max_attribute = Attribute::optional) const override;$/;"	m	class:popl::Attribute::BashCompletionOptionPrinter
+override	util/popl.hpp	/^    std::string print(const Attribute& max_attribute = Attribute::optional) const override;$/;"	m	class:popl::Attribute::ConsoleOptionPrinter
+override	util/popl.hpp	/^    std::string print(const Attribute& max_attribute = Attribute::optional) const override;$/;"	m	class:popl::Attribute::GroffOptionPrinter
+override	util/popl.hpp	/^    void clear() override;$/;"	m	class:popl::Attribute::Value
+override	util/popl.hpp	/^    void parse(OptionName what_name, const char* value) override;$/;"	m	class:popl::Attribute::Implicit
+override	util/popl.hpp	/^    void parse(OptionName what_name, const char* value) override;$/;"	m	class:popl::Attribute::Switch
+override	util/popl.hpp	/^    void parse(OptionName what_name, const char* value) override;$/;"	m	class:popl::Attribute::Value
+override	util/popl.hpp	/^    ~BashCompletionOptionPrinter() override = default;$/;"	m	class:popl::Attribute::BashCompletionOptionPrinter
+override	util/popl.hpp	/^    ~ConsoleOptionPrinter() override = default;$/;"	m	class:popl::Attribute::ConsoleOptionPrinter
+override	util/popl.hpp	/^    ~GroffOptionPrinter() override = default;$/;"	m	class:popl::Attribute::GroffOptionPrinter
+parse	util/popl.hpp	/^inline void Implicit<T>::parse(OptionName what_name, const char* value)$/;"	f	class:popl::Attribute::Implicit
+parse	util/popl.hpp	/^inline void OptionParser::parse(const std::string& ini_filename)$/;"	f	class:popl::Attribute::OptionParser
+parse	util/popl.hpp	/^inline void OptionParser::parse(int argc, const char* const argv[])$/;"	f	class:popl::Attribute::OptionParser
+parse	util/popl.hpp	/^inline void Switch::parse(OptionName \/*what_name*\/, const char* \/*value*\/)$/;"	f	class:popl::Attribute::Switch
+parse	util/popl.hpp	/^inline void Value<T>::parse(OptionName what_name, const char* value)$/;"	f	class:popl::Attribute::Value
+parse	util/popl.hpp	/^inline void Value<bool>::parse(OptionName \/*what_name*\/, const char* value)$/;"	f	class:popl::Attribute::Value
+parse	util/popl.hpp	/^inline void Value<std::string>::parse(OptionName what_name, const char* value)$/;"	f	class:popl::Attribute::Value
+popl	util/popl.hpp	/^namespace popl$/;"	n
+pos_iterator_t	util/MinigridGrammar.h	/^typedef boost::spirit::line_pos_iterator<std::string::const_iterator> pos_iterator_t;$/;"	t
+print	util/popl.hpp	/^inline std::string BashCompletionOptionPrinter::print(const Attribute& \/*max_attribute*\/) const$/;"	f	class:popl::Attribute::BashCompletionOptionPrinter
+print	util/popl.hpp	/^inline std::string ConsoleOptionPrinter::print(const Attribute& max_attribute) const$/;"	f	class:popl::Attribute::ConsoleOptionPrinter
+print	util/popl.hpp	/^inline std::string GroffOptionPrinter::print(const Attribute& max_attribute) const$/;"	f	class:popl::Attribute::GroffOptionPrinter
+printActionsForKeys	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printActionsForKeys(std::ostream &os, cells keys) {$/;"	f	class:prism::PrismModulesPrinter
+printBackgroundLabels	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printBackgroundLabels(std::ostream &os, const AgentName &agentName, const std::pair<Color, cells> &backgroundTiles) {$/;"	f	class:prism::PrismModulesPrinter
+printBooleansForKeys	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printBooleansForKeys(std::ostream &os, const cells &keys) {$/;"	f	class:prism::PrismModulesPrinter
+printCrashLabel	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printCrashLabel(std::ostream &os, const std::vector<AgentName> agentNames) {$/;"	f	class:prism::PrismModulesPrinter
+printEndmodule	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printEndmodule(std::ostream &os) {$/;"	f	class:prism::PrismModulesPrinter
+printFormulas	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printFormulas(std::ostream& os,$/;"	f	class:prism::PrismModulesPrinter
+printGlobalMoveVariable	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printGlobalMoveVariable(std::ostream &os, const size_t &numberOfPlayer) {$/;"	f	class:prism::PrismModulesPrinter
+printGoalLabel	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printGoalLabel(std::ostream& os, const AgentName &agentName, const cells &goals) {$/;"	f	class:prism::PrismModulesPrinter
+printIsOnSlipperyFormula	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printIsOnSlipperyFormula(std::ostream& os, const AgentName &agentName, const cells &slippery) {$/;"	f	class:prism::PrismModulesPrinter
+printKeysLabels	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printKeysLabels(std::ostream& os, const AgentName &agentName, const cells &keys) {$/;"	f	class:prism::PrismModulesPrinter
+printModel	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printModel(std::ostream &os, const ModelType &modelType) {$/;"	f	class:prism::PrismModulesPrinter
+printModule	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printModule(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &boundaries, const coordinates& initialPosition, const cells &keys) {$/;"	f	class:prism::PrismModulesPrinter
+printPlayerStruct	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printPlayerStruct(std::ostream &os, const AgentName &agentName) {$/;"	f	class:prism::PrismModulesPrinter
+printPoplException	util/OptionParser.cpp	/^  int printPoplException(const popl::invalid_option &e) {$/;"	f	namespace:io
+printRestrictionFormula	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printRestrictionFormula(std::ostream& os, const AgentName &agentName, const std::string &direction,  const cells &cells) {$/;"	f	class:prism::PrismModulesPrinter
+printSlipperyMoveEast	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printSlipperyMoveEast(std::ostream &os, const coordinates &c, const bool &blockedNorthEast, const bool &blockedSouthEast) {$/;"	f	class:prism::PrismModulesPrinter
+printSlipperyMoveNorth	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printSlipperyMoveNorth(std::ostream &os, const coordinates &c, const bool &blockedNorthEast, const bool &blockedNorthWest) {$/;"	f	class:prism::PrismModulesPrinter
+printSlipperyMoveSouth	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printSlipperyMoveSouth(std::ostream &os, const coordinates &c, const bool &blockedSouthEast, const bool &blockedSouthWest) {$/;"	f	class:prism::PrismModulesPrinter
+printSlipperyMoveWest	util/PrismModulesPrinter.cpp	/^  std::ostream& PrismModulesPrinter::printSlipperyMoveWest(std::ostream &os, const coordinates &c, const bool &blockedNorthWest, const bool &blockedSouthWest) {$/;"	f	class:prism::PrismModulesPrinter
+printToPrism	util/Grid.cpp	/^void Grid::printToPrism(std::ostream& os, const prism::ModelType& modelType) {$/;"	f	class:Grid
+print_info	main.cpp	/^void print_info(boost::spirit::info const& what) {$/;"	f
+printer	main.cpp	/^struct printer {$/;"	s	file:
+prism	util/PrismModulesPrinter.cpp	/^namespace prism {$/;"	n	file:
+prism	util/PrismModulesPrinter.h	/^namespace prism {$/;"	n
+prism	util/PrismPrinter.h	/^namespace prism {$/;"	n
+program_name_	util/popl.hpp	/^    std::string program_name_;$/;"	m	class:popl::Attribute::BashCompletionOptionPrinter
+qnxnto	build/CMakeFiles/3.16.3/CompilerIdCXX/CMakeCXXCompilerId.cpp	/^char const* qnxnto = "INFO" ":" "qnxnto[]";$/;"	v
+required	util/popl.hpp	/^    required = 2,$/;"	m	class:popl::Attribute
+required	util/popl.hpp	/^    required, \/\/ option always requires an argument$/;"	m	class:popl::Argument
+reset	util/popl.hpp	/^inline void OptionParser::reset()$/;"	f	class:popl::Attribute::OptionParser
+result_type	util/MinigridGrammar.h	/^    typedef void result_type;$/;"	t	struct:annotation_f
+row	util/MinigridGrammar.h	/^typedef std::vector<cell> row;$/;"	t
+row	util/cell.h	/^    int row;$/;"	m	class:Type::Color::cell
+row_	util/MinigridGrammar.h	/^    qi::rule<It, row()>  row_;$/;"	m	struct:MinigridParser
+set_attribute	util/popl.hpp	/^inline void Option::set_attribute(const Attribute& attribute)$/;"	f	class:popl::Attribute::Option
+set_default	util/popl.hpp	/^inline void Value<T>::set_default(const T& value)$/;"	f	class:popl::Attribute::Value
+set_value	util/popl.hpp	/^inline void Value<T>::set_value(const T& value)$/;"	f	class:popl::Attribute::Value
+short_name	util/popl.hpp	/^    short_name,$/;"	m	class:popl::Attribute::OptionName
+short_name	util/popl.hpp	/^inline char Option::short_name() const$/;"	f	class:popl::Attribute::Option
+short_name_	util/popl.hpp	/^    std::string short_name_;$/;"	m	class:popl::Attribute::Option
+slippery	util/Grid.h	/^    cells slippery;$/;"	m	class:Grid
+string	main.cpp	/^    typedef boost::spirit::utf8_string string;$/;"	t	struct:printer	file:
+to_string	util/popl.hpp	/^inline std::string ConsoleOptionPrinter::to_string(Option_ptr option) const$/;"	f	class:popl::Attribute::ConsoleOptionPrinter
+to_string	util/popl.hpp	/^inline std::string GroffOptionPrinter::to_string(Option_ptr option) const$/;"	f	class:popl::Attribute::GroffOptionPrinter
+too_many_arguments	util/popl.hpp	/^        too_many_arguments,$/;"	m	class:popl::Attribute::invalid_option::Error
+type	util/cell.h	/^    Type type;$/;"	m	class:Type::Color::cell
+type_	util/MinigridGrammar.h	/^    qi::symbols<char, Type>  type_;$/;"	m	struct:MinigridParser
+unknown_options	util/popl.hpp	/^inline const std::vector<std::string>& OptionParser::unknown_options() const$/;"	f	class:popl::Attribute::OptionParser
+unknown_options_	util/popl.hpp	/^    std::vector<std::string> unknown_options_;$/;"	m	class:popl::Attribute::OptionParser
+unspecified	util/popl.hpp	/^    unspecified,$/;"	m	class:popl::Attribute::OptionName
+update_reference	util/popl.hpp	/^inline void Value<T>::update_reference()$/;"	f	class:popl::Attribute::Value
+value	util/popl.hpp	/^    std::string value() const$/;"	f	class:popl::Attribute::invalid_option
+value	util/popl.hpp	/^    void set_default(const bool& value) = delete;$/;"	m	class:popl::Attribute::Switch
+value	util/popl.hpp	/^inline T Value<T>::value(size_t idx) const$/;"	f	class:popl::Attribute::Value
+value_	util/popl.hpp	/^    std::string value_;$/;"	m	class:popl::Attribute::invalid_option
+value_or	util/popl.hpp	/^inline T Value<T>::value_or(const T& default_value, size_t idx) const$/;"	f	class:popl::Attribute::Value
+values_	util/popl.hpp	/^    std::vector<T> values_;$/;"	m	class:popl::Attribute::Value
+viewVariable	util/PrismModulesPrinter.cpp	/^  std::string PrismModulesPrinter::viewVariable(const AgentName &agentName, const size_t &agentDirection) {$/;"	f	class:prism::PrismModulesPrinter
+walls	util/Grid.h	/^    cells walls;$/;"	m	class:Grid
+what_name	util/popl.hpp	/^    OptionName what_name() const$/;"	f	class:popl::Attribute::invalid_option
+what_name_	util/popl.hpp	/^    OptionName what_name_;$/;"	m	class:popl::Attribute::invalid_option

Minigrid2PRISM/test.prism

@@ -0,0 +1,303 @@
+//
+// WGWGWGWGWGWGWGWGWGWGWGWGWG
+// WGXR                    WG
+// WG  WGWGWGWGWGWGWGWGWG  WG
+// WG                    SBWG
+// WGZGSB  SBSB  SB  SB    WG
+// WG                  SB  WG
+// WG      SB              WG
+// WG    SB    SB    SB  ZBWG
+// WG  SB                  WG
+// WG      SB    SB    SB  WG
+// WG  WGWGWGWGWGWGWGWGWG  WG
+// WG                    GRWG
+// WGWGWGWGWGWGWGWGWGWGWGWGWG
+//
+// A string representation of a farama minigrid (https://minigrid.farama.org/) with
+// an agent (XR) and adversaries (ZG, ZB).
+//
+// The agent has to move to its goal (GR) while avoiding the adversaries. The agent experiences a stochastic update on a slippery tile (SB).
+
+smg
+
+
+global move : [0..2] init 0;
+
+label AgentOnBlue = (xAgent=1&yAgent=1) | (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=3&yAgent=11);
+label BlueOnBlue = (xBlue=1&yBlue=1) | (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=3&yBlue=11);
+label GreenOnBlue = (xGreen=1&yGreen=1) | (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=3&yGreen=11);
+label AgentOnGreen = (xAgent=9&yAgent=1) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=9&yAgent=10) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+label BlueOnGreen = (xBlue=9&yBlue=1) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=9&yBlue=10) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+label GreenOnGreen = (xGreen=9&yGreen=1) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=9&yGreen=10) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+label AgentOnRed = (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5);
+label BlueOnRed = (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5);
+label GreenOnRed = (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5);
+formula AgentCannotMoveNorth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=1&yAgent=11) | (xAgent=3&yAgent=2) | (xAgent=3&yAgent=3) | (xAgent=3&yAgent=4) | (xAgent=3&yAgent=5) | (xAgent=3&yAgent=6) | (xAgent=3&yAgent=7) | (xAgent=3&yAgent=8) | (xAgent=3&yAgent=9) | (xAgent=3&yAgent=10) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=1&yAgent=1);
+formula AgentCannotMoveEast  = (xAgent=1&yAgent=11) | (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=4&yAgent=11) | (xAgent=5&yAgent=11) | (xAgent=6&yAgent=11) | (xAgent=8&yAgent=11) | (xAgent=9&yAgent=11) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=11) | (xAgent=7&yAgent=11);
+formula AgentCannotMoveSouth = (xAgent=1&yAgent=2) | (xAgent=1&yAgent=3) | (xAgent=1&yAgent=4) | (xAgent=1&yAgent=5) | (xAgent=1&yAgent=6) | (xAgent=1&yAgent=7) | (xAgent=1&yAgent=8) | (xAgent=1&yAgent=9) | (xAgent=1&yAgent=10) | (xAgent=9&yAgent=2) | (xAgent=9&yAgent=3) | (xAgent=9&yAgent=5) | (xAgent=9&yAgent=6) | (xAgent=9&yAgent=8) | (xAgent=9&yAgent=9) | (xAgent=11&yAgent=1) | (xAgent=11&yAgent=2) | (xAgent=11&yAgent=3) | (xAgent=11&yAgent=4) | (xAgent=11&yAgent=5) | (xAgent=11&yAgent=6) | (xAgent=11&yAgent=7) | (xAgent=11&yAgent=8) | (xAgent=11&yAgent=9) | (xAgent=11&yAgent=10) | (xAgent=11&yAgent=11);
+formula AgentCannotMoveWest  = (xAgent=2&yAgent=1) | (xAgent=2&yAgent=11) | (xAgent=3&yAgent=1) | (xAgent=5&yAgent=1) | (xAgent=6&yAgent=1) | (xAgent=7&yAgent=1) | (xAgent=8&yAgent=1) | (xAgent=9&yAgent=1) | (xAgent=10&yAgent=1) | (xAgent=10&yAgent=11) | (xAgent=11&yAgent=1) | (xAgent=1&yAgent=1) | (xAgent=4&yAgent=1);
+formula AgentIsOnSlippery = (xAgent=3&yAgent=11) | (xAgent=4&yAgent=2) | (xAgent=4&yAgent=4) | (xAgent=4&yAgent=5) | (xAgent=4&yAgent=7) | (xAgent=4&yAgent=9) | (xAgent=5&yAgent=10) | (xAgent=6&yAgent=4) | (xAgent=7&yAgent=3) | (xAgent=7&yAgent=6) | (xAgent=7&yAgent=9) | (xAgent=8&yAgent=2) | (xAgent=9&yAgent=4) | (xAgent=9&yAgent=7) | (xAgent=9&yAgent=10);
+
+label AgentGoal = (xAgent=11&yAgent=11);
+formula BlueCannotMoveNorth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=1&yBlue=11) | (xBlue=3&yBlue=2) | (xBlue=3&yBlue=3) | (xBlue=3&yBlue=4) | (xBlue=3&yBlue=5) | (xBlue=3&yBlue=6) | (xBlue=3&yBlue=7) | (xBlue=3&yBlue=8) | (xBlue=3&yBlue=9) | (xBlue=3&yBlue=10) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=1&yBlue=1);
+formula BlueCannotMoveEast  = (xBlue=1&yBlue=11) | (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=4&yBlue=11) | (xBlue=5&yBlue=11) | (xBlue=6&yBlue=11) | (xBlue=8&yBlue=11) | (xBlue=9&yBlue=11) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=11) | (xBlue=7&yBlue=11);
+formula BlueCannotMoveSouth = (xBlue=1&yBlue=2) | (xBlue=1&yBlue=3) | (xBlue=1&yBlue=4) | (xBlue=1&yBlue=5) | (xBlue=1&yBlue=6) | (xBlue=1&yBlue=7) | (xBlue=1&yBlue=8) | (xBlue=1&yBlue=9) | (xBlue=1&yBlue=10) | (xBlue=9&yBlue=2) | (xBlue=9&yBlue=3) | (xBlue=9&yBlue=5) | (xBlue=9&yBlue=6) | (xBlue=9&yBlue=8) | (xBlue=9&yBlue=9) | (xBlue=11&yBlue=1) | (xBlue=11&yBlue=2) | (xBlue=11&yBlue=3) | (xBlue=11&yBlue=4) | (xBlue=11&yBlue=5) | (xBlue=11&yBlue=6) | (xBlue=11&yBlue=7) | (xBlue=11&yBlue=8) | (xBlue=11&yBlue=9) | (xBlue=11&yBlue=10) | (xBlue=11&yBlue=11);
+formula BlueCannotMoveWest  = (xBlue=2&yBlue=1) | (xBlue=2&yBlue=11) | (xBlue=3&yBlue=1) | (xBlue=5&yBlue=1) | (xBlue=6&yBlue=1) | (xBlue=7&yBlue=1) | (xBlue=8&yBlue=1) | (xBlue=9&yBlue=1) | (xBlue=10&yBlue=1) | (xBlue=10&yBlue=11) | (xBlue=11&yBlue=1) | (xBlue=1&yBlue=1) | (xBlue=4&yBlue=1);
+formula BlueIsOnSlippery = (xBlue=3&yBlue=11) | (xBlue=4&yBlue=2) | (xBlue=4&yBlue=4) | (xBlue=4&yBlue=5) | (xBlue=4&yBlue=7) | (xBlue=4&yBlue=9) | (xBlue=5&yBlue=10) | (xBlue=6&yBlue=4) | (xBlue=7&yBlue=3) | (xBlue=7&yBlue=6) | (xBlue=7&yBlue=9) | (xBlue=8&yBlue=2) | (xBlue=9&yBlue=4) | (xBlue=9&yBlue=7) | (xBlue=9&yBlue=10);
+
+label BlueGoal = (xBlue=11&yBlue=11);
+formula GreenCannotMoveNorth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=1&yGreen=11) | (xGreen=3&yGreen=2) | (xGreen=3&yGreen=3) | (xGreen=3&yGreen=4) | (xGreen=3&yGreen=5) | (xGreen=3&yGreen=6) | (xGreen=3&yGreen=7) | (xGreen=3&yGreen=8) | (xGreen=3&yGreen=9) | (xGreen=3&yGreen=10) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=1&yGreen=1);
+formula GreenCannotMoveEast  = (xGreen=1&yGreen=11) | (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=4&yGreen=11) | (xGreen=5&yGreen=11) | (xGreen=6&yGreen=11) | (xGreen=8&yGreen=11) | (xGreen=9&yGreen=11) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=11) | (xGreen=7&yGreen=11);
+formula GreenCannotMoveSouth = (xGreen=1&yGreen=2) | (xGreen=1&yGreen=3) | (xGreen=1&yGreen=4) | (xGreen=1&yGreen=5) | (xGreen=1&yGreen=6) | (xGreen=1&yGreen=7) | (xGreen=1&yGreen=8) | (xGreen=1&yGreen=9) | (xGreen=1&yGreen=10) | (xGreen=9&yGreen=2) | (xGreen=9&yGreen=3) | (xGreen=9&yGreen=5) | (xGreen=9&yGreen=6) | (xGreen=9&yGreen=8) | (xGreen=9&yGreen=9) | (xGreen=11&yGreen=1) | (xGreen=11&yGreen=2) | (xGreen=11&yGreen=3) | (xGreen=11&yGreen=4) | (xGreen=11&yGreen=5) | (xGreen=11&yGreen=6) | (xGreen=11&yGreen=7) | (xGreen=11&yGreen=8) | (xGreen=11&yGreen=9) | (xGreen=11&yGreen=10) | (xGreen=11&yGreen=11);
+formula GreenCannotMoveWest  = (xGreen=2&yGreen=1) | (xGreen=2&yGreen=11) | (xGreen=3&yGreen=1) | (xGreen=5&yGreen=1) | (xGreen=6&yGreen=1) | (xGreen=7&yGreen=1) | (xGreen=8&yGreen=1) | (xGreen=9&yGreen=1) | (xGreen=10&yGreen=1) | (xGreen=10&yGreen=11) | (xGreen=11&yGreen=1) | (xGreen=1&yGreen=1) | (xGreen=4&yGreen=1);
+formula GreenIsOnSlippery = (xGreen=3&yGreen=11) | (xGreen=4&yGreen=2) | (xGreen=4&yGreen=4) | (xGreen=4&yGreen=5) | (xGreen=4&yGreen=7) | (xGreen=4&yGreen=9) | (xGreen=5&yGreen=10) | (xGreen=6&yGreen=4) | (xGreen=7&yGreen=3) | (xGreen=7&yGreen=6) | (xGreen=7&yGreen=9) | (xGreen=8&yGreen=2) | (xGreen=9&yGreen=4) | (xGreen=9&yGreen=7) | (xGreen=9&yGreen=10);
+
+label GreenGoal = (xGreen=11&yGreen=11);
+label crash = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+
+formula crashHappened = (xAgent=xBlue)&(yAgent=yBlue) | (xAgent=xGreen)&(yAgent=yGreen);
+module Agent
+	xAgent : [1..12] init 1;
+	yAgent : [1..12] init 1;
+
+	viewAgent : [0..3] init 1;
+
+	[Agent_turn_right] !crashHappened & move=0 &  true -> (viewAgent'=mod(viewAgent + 1, 4))  & (move'=1) ;
+	[Agent_turn_left]  !crashHappened & move=0 &  viewAgent>0 -> (viewAgent'=viewAgent - 1)  & (move'=1) ;
+	[Agent_turn_left]  !crashHappened & move=0 &  viewAgent=0 -> (viewAgent'=3)  & (move'=1) ;
+
+
+	[Agent_move_north] !crashHappened & move=0 &  viewAgent=3 &  !AgentIsOnSlippery & !AgentCannotMoveNorth -> (xAgent'=xAgent-1) & (move'=1) ;
+	[Agent_move_east]  !crashHappened & move=0 &  viewAgent=0 &  !AgentIsOnSlippery & !AgentCannotMoveEast  -> (yAgent'=yAgent+1) & (move'=1) ;
+	[Agent_move_south] !crashHappened & move=0 &  viewAgent=1 &  !AgentIsOnSlippery & !AgentCannotMoveSouth -> (xAgent'=xAgent+1) & (move'=1) ;
+	[Agent_move_west]  !crashHappened & move=0 &  viewAgent=2 &  !AgentIsOnSlippery & !AgentCannotMoveWest  -> (yAgent'=yAgent-1) & (move'=1) ;
+
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=3 & yAgent=11 -> 3/3 : (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=3 & yAgent=11 -> 3/3 : (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=3 & yAgent=11 -> 2/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=3 & yAgent=11 -> 2/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=4 & yAgent=2 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=4 & yAgent=2 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=4 & yAgent=2 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=4 & yAgent=2 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=4 & yAgent=4 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=4 & yAgent=4 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=4 & yAgent=4 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=4 & yAgent=4 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=4 & yAgent=5 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=4 & yAgent=5 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=4 & yAgent=5 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=4 & yAgent=5 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=4 & yAgent=7 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=4 & yAgent=7 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=4 & yAgent=7 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=4 & yAgent=7 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=4 & yAgent=9 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=4 & yAgent=9 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=4 & yAgent=9 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=4 & yAgent=9 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=5 & yAgent=10 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=5 & yAgent=10 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=5 & yAgent=10 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=5 & yAgent=10 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=6 & yAgent=4 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=6 & yAgent=4 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=6 & yAgent=4 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=6 & yAgent=4 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=7 & yAgent=3 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=7 & yAgent=3 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=7 & yAgent=3 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=7 & yAgent=3 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=7 & yAgent=6 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=7 & yAgent=6 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=7 & yAgent=6 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=7 & yAgent=6 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=7 & yAgent=9 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=7 & yAgent=9 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=7 & yAgent=9 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=7 & yAgent=9 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=8 & yAgent=2 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=8 & yAgent=2 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=8 & yAgent=2 -> 1/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=8 & yAgent=2 -> 1/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=9 & yAgent=4 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=9 & yAgent=4 -> 2/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=9 & yAgent=4 -> 3/3 : (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=9 & yAgent=4 -> 2/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=9 & yAgent=7 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=9 & yAgent=7 -> 2/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=9 & yAgent=7 -> 3/3 : (xAgent'=xAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=9 & yAgent=7 -> 2/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_north] !crashHappened & move=0 &  viewAgent=3 & xAgent=9 & yAgent=10 -> 1/3 : (xAgent'=xAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent-1) + 1/3: (xAgent'=xAgent-1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_east]  !crashHappened & move=0 &   viewAgent=0 & xAgent=9 & yAgent=10 -> 1/3 : (yAgent'=yAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent+1) + 1/3: (yAgent'=yAgent+1) & (xAgent'=xAgent-1) & (move'=1) ;
+	[Agentslip_south] !crashHappened & move=0 &  viewAgent=1 & xAgent=9 & yAgent=10 -> 2/3 : (xAgent'=xAgent+1) + 1/3: (xAgent'=xAgent+1) & (yAgent'=yAgent+1) & (move'=1) ;
+	[Agentslip_west]  !crashHappened & move=0 &   viewAgent=2 & xAgent=9 & yAgent=10 -> 2/3 : (yAgent'=yAgent-1) + 1/3: (yAgent'=yAgent-1) & (xAgent'=xAgent-1) & (move'=1) ;
+
+  [crashed] move=0 & crashHappened -> true;
+endmodule
+
+player Agent
+	[Agent_move_north], [Agent_move_east], [Agent_move_south], [Agent_move_west], [Agent_turn_left], [Agent_turn_right], 	[Agentslip_east] , 	[Agentslip_north], 	[Agentslip_south], 	[Agentslip_west], [crashed]
+endplayer
+module Blue
+	xBlue : [1..12] init 7;
+	yBlue : [1..12] init 11;
+
+	viewBlue : [0..3] init 1;
+
+	[Blue_turn_right]  move=1 &  true -> (viewBlue'=mod(viewBlue + 1, 4))  & (move'=2) ;
+	[Blue_turn_left]   move=1 &  viewBlue>0 -> (viewBlue'=viewBlue - 1)  & (move'=2) ;
+	[Blue_turn_left]   move=1 &  viewBlue=0 -> (viewBlue'=3)  & (move'=2) ;
+
+
+	[Blue_move_north] move=1 &  viewBlue=3 &  !BlueIsOnSlippery & !BlueCannotMoveNorth -> (xBlue'=xBlue-1) & (move'=2) ;
+	[Blue_move_east]  move=1 &  viewBlue=0 &  !BlueIsOnSlippery & !BlueCannotMoveEast  -> (yBlue'=yBlue+1) & (move'=2) ;
+	[Blue_move_south] move=1 &  viewBlue=1 &  !BlueIsOnSlippery & !BlueCannotMoveSouth -> (xBlue'=xBlue+1) & (move'=2) ;
+	[Blue_move_west]  move=1 &  viewBlue=2 &  !BlueIsOnSlippery & !BlueCannotMoveWest  -> (yBlue'=yBlue-1) & (move'=2) ;
+
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=3 & yBlue=11 -> 3/3 : (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=3 & yBlue=11 -> 3/3 : (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=3 & yBlue=11 -> 2/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=3 & yBlue=11 -> 2/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=4 & yBlue=2 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=4 & yBlue=2 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=4 & yBlue=2 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=4 & yBlue=2 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=4 & yBlue=4 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=4 & yBlue=4 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=4 & yBlue=4 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=4 & yBlue=4 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=4 & yBlue=5 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=4 & yBlue=5 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=4 & yBlue=5 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=4 & yBlue=5 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=4 & yBlue=7 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=4 & yBlue=7 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=4 & yBlue=7 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=4 & yBlue=7 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=4 & yBlue=9 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=4 & yBlue=9 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=4 & yBlue=9 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=4 & yBlue=9 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=5 & yBlue=10 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=5 & yBlue=10 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=5 & yBlue=10 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=5 & yBlue=10 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=6 & yBlue=4 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=6 & yBlue=4 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=6 & yBlue=4 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=6 & yBlue=4 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=7 & yBlue=3 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=7 & yBlue=3 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=7 & yBlue=3 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=7 & yBlue=3 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=7 & yBlue=6 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=7 & yBlue=6 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=7 & yBlue=6 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=7 & yBlue=6 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=7 & yBlue=9 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=7 & yBlue=9 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=7 & yBlue=9 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=7 & yBlue=9 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=8 & yBlue=2 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=8 & yBlue=2 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=8 & yBlue=2 -> 1/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=8 & yBlue=2 -> 1/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=9 & yBlue=4 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=9 & yBlue=4 -> 2/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=9 & yBlue=4 -> 3/3 : (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=9 & yBlue=4 -> 2/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=9 & yBlue=7 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=9 & yBlue=7 -> 2/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=9 & yBlue=7 -> 3/3 : (xBlue'=xBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=9 & yBlue=7 -> 2/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_north] move=1 &  viewBlue=3 & xBlue=9 & yBlue=10 -> 1/3 : (xBlue'=xBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue-1) + 1/3: (xBlue'=xBlue-1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_east]  move=1 &   viewBlue=0 & xBlue=9 & yBlue=10 -> 1/3 : (yBlue'=yBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue+1) + 1/3: (yBlue'=yBlue+1) & (xBlue'=xBlue-1) & (move'=2) ;
+	[Blueslip_south] move=1 &  viewBlue=1 & xBlue=9 & yBlue=10 -> 2/3 : (xBlue'=xBlue+1) + 1/3: (xBlue'=xBlue+1) & (yBlue'=yBlue+1) & (move'=2) ;
+	[Blueslip_west]  move=1 &   viewBlue=2 & xBlue=9 & yBlue=10 -> 2/3 : (yBlue'=yBlue-1) + 1/3: (yBlue'=yBlue-1) & (xBlue'=xBlue-1) & (move'=2) ;
+endmodule
+
+player Blue
+	[Blue_move_north], [Blue_move_east], [Blue_move_south], [Blue_move_west], [Blue_turn_left], [Blue_turn_right], 	[Blueslip_east] , 	[Blueslip_north], 	[Blueslip_south], 	[Blueslip_west]
+endplayer
+module Green
+	xGreen : [1..12] init 4;
+	yGreen : [1..12] init 1;
+
+	viewGreen : [0..3] init 1;
+
+	[Green_turn_right]  move=2 &  true -> (viewGreen'=mod(viewGreen + 1, 4))  & (move'=0) ;
+	[Green_turn_left]   move=2 &  viewGreen>0 -> (viewGreen'=viewGreen - 1)  & (move'=0) ;
+	[Green_turn_left]   move=2 &  viewGreen=0 -> (viewGreen'=3)  & (move'=0) ;
+
+
+	[Green_move_north] move=2 &  viewGreen=3 &  !GreenIsOnSlippery & !GreenCannotMoveNorth -> (xGreen'=xGreen-1) & (move'=0) ;
+	[Green_move_east]  move=2 &  viewGreen=0 &  !GreenIsOnSlippery & !GreenCannotMoveEast  -> (yGreen'=yGreen+1) & (move'=0) ;
+	[Green_move_south] move=2 &  viewGreen=1 &  !GreenIsOnSlippery & !GreenCannotMoveSouth -> (xGreen'=xGreen+1) & (move'=0) ;
+	[Green_move_west]  move=2 &  viewGreen=2 &  !GreenIsOnSlippery & !GreenCannotMoveWest  -> (yGreen'=yGreen-1) & (move'=0) ;
+
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=3 & yGreen=11 -> 3/3 : (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=3 & yGreen=11 -> 3/3 : (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=3 & yGreen=11 -> 2/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=3 & yGreen=11 -> 2/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=4 & yGreen=2 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=4 & yGreen=2 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=4 & yGreen=2 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=4 & yGreen=2 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=4 & yGreen=4 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=4 & yGreen=4 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=4 & yGreen=4 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=4 & yGreen=4 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=4 & yGreen=5 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=4 & yGreen=5 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=4 & yGreen=5 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=4 & yGreen=5 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=4 & yGreen=7 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=4 & yGreen=7 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=4 & yGreen=7 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=4 & yGreen=7 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=4 & yGreen=9 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=4 & yGreen=9 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=4 & yGreen=9 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=4 & yGreen=9 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=5 & yGreen=10 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=5 & yGreen=10 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=5 & yGreen=10 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=5 & yGreen=10 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=6 & yGreen=4 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=6 & yGreen=4 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=6 & yGreen=4 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=6 & yGreen=4 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=7 & yGreen=3 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=7 & yGreen=3 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=7 & yGreen=3 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=7 & yGreen=3 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=7 & yGreen=6 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=7 & yGreen=6 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=7 & yGreen=6 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=7 & yGreen=6 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=7 & yGreen=9 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=7 & yGreen=9 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=7 & yGreen=9 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=7 & yGreen=9 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=8 & yGreen=2 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=8 & yGreen=2 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=8 & yGreen=2 -> 1/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=8 & yGreen=2 -> 1/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=9 & yGreen=4 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=9 & yGreen=4 -> 2/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=9 & yGreen=4 -> 3/3 : (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=9 & yGreen=4 -> 2/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=9 & yGreen=7 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=9 & yGreen=7 -> 2/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=9 & yGreen=7 -> 3/3 : (xGreen'=xGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=9 & yGreen=7 -> 2/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_north] move=2 &  viewGreen=3 & xGreen=9 & yGreen=10 -> 1/3 : (xGreen'=xGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen-1) + 1/3: (xGreen'=xGreen-1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_east]  move=2 &   viewGreen=0 & xGreen=9 & yGreen=10 -> 1/3 : (yGreen'=yGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen+1) + 1/3: (yGreen'=yGreen+1) & (xGreen'=xGreen-1) & (move'=0) ;
+	[Greenslip_south] move=2 &  viewGreen=1 & xGreen=9 & yGreen=10 -> 2/3 : (xGreen'=xGreen+1) + 1/3: (xGreen'=xGreen+1) & (yGreen'=yGreen+1) & (move'=0) ;
+	[Greenslip_west]  move=2 &   viewGreen=2 & xGreen=9 & yGreen=10 -> 2/3 : (yGreen'=yGreen-1) + 1/3: (yGreen'=yGreen-1) & (xGreen'=xGreen-1) & (move'=0) ;
+endmodule
+
+player Green
+	[Green_move_north], [Green_move_east], [Green_move_south], [Green_move_west], [Green_turn_left], [Green_turn_right], 	[Greenslip_east] , 	[Greenslip_north], 	[Greenslip_south], 	[Greenslip_west]
+endplayer

Minigrid2PRISM/test.prop

@@ -0,0 +1 @@
+<<Agent>> Pmax=? [ F "AgentGoal" ];

Minigrid2PRISM/testGrid

@@ -0,0 +1,5 @@
+WGWGWGWGWG
+WGSR    WG
+WG  WG  WG
+WG      WG
+WGWGWGWGWG

Minigrid2PRISM/testGridWithSlippery

@@ -0,0 +1,6 @@
+WGWGWGWGWGWGWGWGWGWGWG
+WGSB      LB        WG
+WG  SB    WG        WG
+WG        WG        WG
+WG        WG        WG
+WGWGWGWGWGWGWGWGWGWGWG

Minigrid2PRISM/testGridWithSlipperyAnnotated

@@ -0,0 +1,7 @@
+  1 2 3 4 5 6 7 8 9 1011
+1 WGWGWGWGWGWGWGWGWGWGWG
+2 WGSB      LB        WG
+3 WG  SB    WG        WG
+4 WG        WG        WG
+5 WG        WG        WG
+6 WGWGWGWGDPWGWGWGWGWGWG

Minigrid2PRISM/util/CMakeLists.txt

@@ -0,0 +1,8 @@
+list(APPEND SRCS
+  ${CMAKE_CURRENT_LIST_DIR}/cell.cpp
+  ${CMAKE_CURRENT_LIST_DIR}/MinigridGrammar.h
+  ${CMAKE_CURRENT_LIST_DIR}/Grid.cpp
+  ${CMAKE_CURRENT_LIST_DIR}/PrismModulesPrinter.cpp
+  ${CMAKE_CURRENT_LIST_DIR}/popl.hpp
+  ${CMAKE_CURRENT_LIST_DIR}/OptionParser.cpp
+)

Minigrid2PRISM/util/Grid.cpp

@@ -0,0 +1,212 @@
+#include "Grid.h"
+
+#include <algorithm>
+
+Grid::Grid(cells gridCells, cells background, const GridOptions &gridOptions, const std::map<coordinates, float> &stateRewards)
+  : allGridCells(gridCells), background(background), gridOptions(gridOptions), stateRewards(stateRewards)
+{
+  cell max = allGridCells.at(allGridCells.size() - 1);
+  maxBoundaries = std::make_pair(max.row, max.column);
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(walls), [](cell c) {
+      return c.type == Type::Wall;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(lava), [](cell c) {
+      return c.type == Type::Lava;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(floor), [](cell c) {
+      return c.type == Type::Floor;
+  });
+  std::copy_if(background.begin(), background.end(), std::back_inserter(slipperyNorth), [](cell c) {
+      return c.type == Type::SlipperyNorth;
+  });
+  std::copy_if(background.begin(), background.end(), std::back_inserter(slipperyEast), [](cell c) {
+      return c.type == Type::SlipperyEast;
+  });
+  std::copy_if(background.begin(), background.end(), std::back_inserter(slipperySouth), [](cell c) {
+      return c.type == Type::SlipperySouth;
+  });
+  std::copy_if(background.begin(), background.end(), std::back_inserter(slipperyWest), [](cell c) {
+      return c.type == Type::SlipperyWest;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(lockedDoors), [](cell c) {
+      return c.type == Type::LockedDoor;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(goals), [](cell c) {
+      return c.type == Type::Goal;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(keys), [](cell c) {
+      return c.type == Type::Key;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(boxes), [](cell c) {
+      return c.type == Type::Box;
+  });
+  agent = *std::find_if(gridCells.begin(), gridCells.end(), [](cell c) {
+      return c.type == Type::Agent;
+  });
+  std::copy_if(gridCells.begin(), gridCells.end(), std::back_inserter(adversaries), [](cell c) {
+      return c.type == Type::Adversary;
+  });
+  agentNameAndPositionMap.insert({ "Agent", agent.getCoordinates() });
+  for(auto const& adversary : adversaries) {
+    std::string color = adversary.getColor();
+    color.at(0) = std::toupper(color.at(0));
+    try {
+      if(gridOptions.agentsToBeConsidered.size() != 0 && std::find(gridOptions.agentsToBeConsidered.begin(), gridOptions.agentsToBeConsidered.end(), color) == gridOptions.agentsToBeConsidered.end()) continue;
+      auto success = agentNameAndPositionMap.insert({ color, adversary.getCoordinates() });
+      if(!success.second) {
+        throw std::logic_error("Agent with " + color + " already present\n");
+      }
+    } catch(const std::logic_error& e) {
+      std::cerr << "Expected agents colors to be different. Agent with color : '" << color << "' already present." << std::endl;
+      throw;
+    }
+  }
+  for(auto const& color : allColors) {
+    cells cellsOfColor;
+    std::copy_if(background.begin(), background.end(), std::back_inserter(cellsOfColor), [&color](cell c) {
+        return c.type == Type::Floor && c.color == color;
+    });
+    backgroundTiles.emplace(color, cellsOfColor);
+  }
+}
+
+std::ostream& operator<<(std::ostream& os, const Grid& grid) {
+  int lastRow = 1;
+  for(auto const& cell : grid.allGridCells) {
+    if(lastRow != cell.row)
+      os << std::endl;
+    os << static_cast<char>(cell.type) << static_cast<char>(cell.color);
+    lastRow = cell.row;
+  }
+  return os;
+}
+
+cells Grid::getGridCells() {
+  return allGridCells;
+}
+
+bool Grid::isBlocked(coordinates p) {
+  return isWall(p) || isLockedDoor(p) || isKey(p);
+}
+
+bool Grid::isWall(coordinates p) {
+  return std::find_if(walls.begin(), walls.end(),
+      [p](cell cell) {
+        return cell.row == p.first && cell.column == p.second;
+      }) != walls.end();
+}
+
+bool Grid::isLockedDoor(coordinates p) {
+  return std::find_if(lockedDoors.begin(), lockedDoors.end(),
+      [p](cell cell) {
+        return cell.row == p.first && cell.column == p.second;
+      }) != lockedDoors.end();
+}
+
+bool Grid::isKey(coordinates p) {
+  return std::find_if(keys.begin(), keys.end(),
+      [p](cell cell) {
+        return cell.row == p.first && cell.column == p.second;
+      }) != keys.end();
+}
+
+bool Grid::isBox(coordinates p) {
+  return std::find_if(boxes.begin(), boxes.end(),
+      [p](cell cell) {
+        return cell.row == p.first && cell.column == p.second;
+      }) != boxes.end();
+}
+
+void Grid::printToPrism(std::ostream& os, const prism::ModelType& modelType) {
+  cells northRestriction;
+  cells eastRestriction;
+  cells southRestriction;
+  cells westRestriction;
+  cells walkable = floor;
+  walkable.insert(walkable.end(), goals.begin(), goals.end());
+  walkable.insert(walkable.end(), boxes.begin(), boxes.end());
+  walkable.push_back(agent);
+  walkable.insert(walkable.end(), adversaries.begin(), adversaries.end());
+  walkable.insert(walkable.end(), lava.begin(), lava.end());
+  for(auto const& c : walkable) {
+    if(isBlocked(c.getNorth())) northRestriction.push_back(c);
+    if(isBlocked(c.getEast()))   eastRestriction.push_back(c);
+    if(isBlocked(c.getSouth())) southRestriction.push_back(c);
+    if(isBlocked(c.getWest()))   westRestriction.push_back(c);
+  }
+
+  prism::PrismModulesPrinter printer(modelType, agentNameAndPositionMap.size());
+  printer.printModel(os, modelType);
+  if(modelType == prism::ModelType::SMG) {
+    printer.printGlobalMoveVariable(os, agentNameAndPositionMap.size());
+  }
+  for(auto const &backgroundTilesOfColor : backgroundTiles) {
+    for(auto agentNameAndPosition = agentNameAndPositionMap.begin(); agentNameAndPosition != agentNameAndPositionMap.end(); ++agentNameAndPosition) {
+      printer.printBackgroundLabels(os, agentNameAndPosition->first, backgroundTilesOfColor);
+    }
+  }
+  for(auto agentNameAndPosition = agentNameAndPositionMap.begin(); agentNameAndPosition != agentNameAndPositionMap.end(); ++agentNameAndPosition) {
+    printer.printFormulas(os, agentNameAndPosition->first, northRestriction, eastRestriction, southRestriction, westRestriction, { slipperyNorth, slipperyEast, slipperySouth, slipperyWest }, lava);
+    printer.printGoalLabel(os, agentNameAndPosition->first, goals);
+    printer.printKeysLabels(os, agentNameAndPosition->first, keys);
+  }
+
+  std::vector<AgentName> agentNames;
+  std::transform(agentNameAndPositionMap.begin(),
+                 agentNameAndPositionMap.end(),
+                 std::back_inserter(agentNames),
+                 [](const std::map<AgentNameAndPosition::first_type,AgentNameAndPosition::second_type>::value_type &pair){return pair.first;});
+  if(modelType == prism::ModelType::SMG) {
+    printer.printCrashLabel(os, agentNames);
+  }
+  size_t agentIndex  = 0;
+  for(auto agentNameAndPosition = agentNameAndPositionMap.begin(); agentNameAndPosition != agentNameAndPositionMap.end(); ++agentNameAndPosition, agentIndex++) {
+    AgentName agentName = agentNameAndPosition->first;
+    bool agentWithView = std::find(gridOptions.agentsWithView.begin(), gridOptions.agentsWithView.end(), agentName) != gridOptions.agentsWithView.end();
+    bool agentWithProbabilisticBehaviour = std::find(gridOptions.agentsWithProbabilisticBehaviour.begin(), gridOptions.agentsWithProbabilisticBehaviour.end(), agentName) != gridOptions.agentsWithProbabilisticBehaviour.end();
+    std::set<std::string> slipperyActions;
+    if(agentWithProbabilisticBehaviour) printer.printModule(os, agentName, agentIndex, maxBoundaries, agentNameAndPosition->second, keys, agentWithView, gridOptions.probabilitiesForActions);
+    else                                printer.printModule(os, agentName, agentIndex, maxBoundaries, agentNameAndPosition->second, keys, agentWithView);
+    for(auto const& c : slipperyNorth) {
+      printer.printSlipperyMove(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::North);
+      printer.printSlipperyTurn(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::North);
+
+    }
+    for(auto const& c : slipperyEast) {
+      printer.printSlipperyMove(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::East);
+      printer.printSlipperyTurn(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::East);
+    }
+    for(auto const& c : slipperySouth) {
+      printer.printSlipperyMove(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::South);
+      printer.printSlipperyTurn(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::South);
+    }
+    for(auto const& c : slipperyWest) {
+      printer.printSlipperyMove(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::West);
+      printer.printSlipperyTurn(os, agentName, agentIndex, c.getCoordinates(), slipperyActions, getWalkableDirOf8Neighborhood(c), prism::PrismModulesPrinter::SlipperyType::West);
+    }
+
+    printer.printEndmodule(os);
+    if(modelType == prism::ModelType::SMG) {
+      if(agentWithProbabilisticBehaviour) printer.printPlayerStruct(os, agentNameAndPosition->first, agentWithView, gridOptions.probabilitiesForActions, slipperyActions);
+      else                                printer.printPlayerStruct(os, agentNameAndPosition->first, agentWithView, {}, slipperyActions);
+    }
+    if(!stateRewards.empty()) {
+      printer.printRewards(os, stateRewards, lava, goals);
+    }
+  }
+}
+
+
+std::array<bool, 8> Grid::getWalkableDirOf8Neighborhood(cell c) /* const */ {
+  return (std::array<bool, 8>)
+         {
+           !isBlocked(c.getNorth()),
+           !isBlocked(c.getNorth(allGridCells).getEast()),
+           !isBlocked(c.getEast()),
+           !isBlocked(c.getSouth(allGridCells).getEast()),
+           !isBlocked(c.getSouth()),
+           !isBlocked(c.getSouth(allGridCells).getWest()),
+           !isBlocked(c.getWest()),
+           !isBlocked(c.getNorth(allGridCells).getWest())
+         };
+}

Minigrid2PRISM/util/Grid.h

@@ -0,0 +1,62 @@
+#pragma once
+
+#include <iostream>
+#include <fstream>
+#include <map>
+#include <utility>
+
+#include "MinigridGrammar.h"
+#include "PrismModulesPrinter.h"
+
+struct GridOptions {
+  std::vector<AgentName> agentsToBeConsidered;
+  std::vector<AgentName> agentsWithView;
+  std::vector<AgentName> agentsWithProbabilisticBehaviour;
+  std::vector<float>     probabilitiesForActions;
+};
+
+class Grid {
+  public:
+    Grid(cells gridCells, cells background, const GridOptions &gridOptions, const std::map<coordinates, float> &stateRewards = {});
+
+    cells getGridCells();
+
+    bool isBlocked(coordinates p);
+    bool isWall(coordinates p);
+    bool isLockedDoor(coordinates p);
+    bool isKey(coordinates p);
+    bool isBox(coordinates p);
+    void printToPrism(std::ostream &os, const prism::ModelType& modelType);
+
+    std::array<bool, 8> getWalkableDirOf8Neighborhood(cell c);
+
+    friend std::ostream& operator<<(std::ostream& os, const Grid &grid);
+
+  private:
+    GridOptions gridOptions;
+
+    cells allGridCells;
+    cells background;
+    coordinates maxBoundaries;
+
+    cell agent;
+    cells adversaries;
+    AgentNameAndPositionMap agentNameAndPositionMap;
+
+    cells walls;
+    cells floor;
+    cells slipperyNorth;
+    cells slipperyEast;
+    cells slipperySouth; 
+    cells slipperyWest;
+    cells lockedDoors;
+    cells boxes;
+    cells lava;
+
+    cells goals;
+    cells keys;
+
+    std::map<Color, cells> backgroundTiles;
+
+    std::map<coordinates, float> stateRewards;
+};

Minigrid2PRISM/util/MinigridGrammar.h

@@ -0,0 +1,102 @@
+#pragma once
+
+#include "cell.h"
+
+#include <vector>
+
+#include <boost/tokenizer.hpp>
+#include <boost/fusion/adapted/struct.hpp>
+#include <boost/spirit/include/qi.hpp>
+#include <boost/spirit/include/phoenix.hpp>
+#include <boost/spirit/include/phoenix_operator.hpp>
+#include <boost/variant/recursive_wrapper.hpp>
+#include <boost/spirit/include/support_line_pos_iterator.hpp>
+
+namespace qi      = boost::spirit::qi;
+namespace phoenix = boost::phoenix;
+
+typedef boost::spirit::line_pos_iterator<std::string::const_iterator> pos_iterator_t;
+
+typedef std::vector<cell> row;
+typedef std::vector<cell> cells;
+
+BOOST_FUSION_ADAPT_STRUCT(
+    cell,
+    (Type, type)
+    (Color, color)
+)
+
+template<typename It>
+struct annotation_f {
+    typedef void result_type;
+
+    annotation_f(It first) : first(first) {}
+    It const first;
+
+    template<typename Val, typename First, typename Last>
+    void operator()(Val& v, First f, Last l) const {
+        do_annotate(v, f, l, first);
+    }
+
+  private:
+    void static do_annotate(cell& c, It f, It l, It first) {
+        c.row    = get_line(f) - 1;
+        c.column = get_column(first, f) / 2;
+    }
+    static void do_annotate(...) { std::cerr << "(not having LocationInfo)\n"; }
+};
+
+template <typename It>
+    struct MinigridParser : qi::grammar<It, row()>
+{
+  MinigridParser(It first) : MinigridParser::base_type(row_), annotate(first)
+  {
+    using namespace qi;
+    type_.add
+      ("W", Type::Wall)
+      (" ", Type::Floor)
+      ("D", Type::Door)
+      ("L", Type::LockedDoor)
+      ("K", Type::Key)
+      ("A", Type::Ball)
+      ("B", Type::Box)
+      ("G", Type::Goal)
+      ("V", Type::Lava)
+      ("n", Type::SlipperyNorth)
+      ("e", Type::SlipperyEast)
+      ("s", Type::SlipperySouth)
+      ("w", Type::SlipperyWest)
+      ("X", Type::Agent)
+      ("Z", Type::Adversary);
+    color_.add
+      ("R", Color::Red)
+      ("G", Color::Green)
+      ("B", Color::Blue)
+      ("P", Color::Purple)
+      ("Y", Color::Yellow)
+      (" ", Color::None);
+
+    cell_ = type_ > color_;
+
+    row_ = (cell_ % -qi::char_("\n"));
+
+    auto set_location_info = annotate(_val, _1, _3);
+    on_success(cell_, set_location_info);
+
+    BOOST_SPIRIT_DEBUG_NODE(type_);
+    BOOST_SPIRIT_DEBUG_NODE(color_);
+    BOOST_SPIRIT_DEBUG_NODE(cell_);
+  }
+
+  private:
+    phoenix::function<annotation_f<It>> annotate;
+
+    qi::symbols<char, Type>  type_;
+    qi::symbols<char, Color> color_;
+
+    qi::rule<It, cell()> cell_;
+    qi::rule<It, row()>  row_;
+};
+
+typedef boost::tokenizer< boost::escaped_list_separator<char> , std::string::const_iterator, std::string> Tokenizer;
+//std::ostream& operator<<(std::ostream& os, const row& r);

Minigrid2PRISM/util/OptionParser.cpp

@@ -0,0 +1,32 @@
+#include <iostream>
+
+#include "popl.hpp"
+#include "OptionParser.h"
+
+namespace io {
+  int printPoplException(const popl::invalid_option &e) {
+    std::cerr << "Invalid Option Exception: " << e.what() << "\n";
+		std::cerr << "error:  ";
+		if (e.error() == popl::invalid_option::Error::missing_argument) {
+			std::cerr << "missing_argument\n";
+    } else if (e.error() == popl::invalid_option::Error::invalid_argument) {
+			std::cerr << "invalid_argument\n";
+    } else if (e.error() == popl::invalid_option::Error::too_many_arguments) {
+			std::cerr << "too_many_arguments\n";
+    } else if (e.error() == popl::invalid_option::Error::missing_option) {
+			std::cerr << "missing_option\n";
+    }
+
+		if (e.error() == popl::invalid_option::Error::missing_option) {
+      std::string option_name(e.option()->name(popl::OptionName::short_name, true));
+			if (option_name.empty())
+				option_name = e.option()->name(popl::OptionName::long_name, true);
+			std::cerr << "option: " << option_name << "\n";
+		}
+		else {
+			std::cerr << "option: " << e.option()->name(e.what_name()) << "\n";
+			std::cerr << "value:  " << e.value() << "\n";
+		}
+		return EXIT_FAILURE;
+  }
+}

Minigrid2PRISM/util/OptionParser.h

@@ -0,0 +1,13 @@
+#pragma once
+
+#include <iostream>
+
+#include "popl.hpp"
+
+#define INPUT_ERROR 1
+#define INPUT_OK 0
+
+namespace io {
+  int printPoplException(const popl::invalid_option &e);
+
+}

Minigrid2PRISM/util/PrismModulesPrinter.cpp

@@ -0,0 +1,496 @@
+#include "PrismModulesPrinter.h"
+
+#include <map>
+#include <string>
+
+namespace prism {
+
+  PrismModulesPrinter::PrismModulesPrinter(const ModelType &modelType, const size_t &numberOfPlayer)
+    : modelType(modelType), numberOfPlayer(numberOfPlayer) {
+  }
+
+  std::ostream& PrismModulesPrinter::printModel(std::ostream &os, const ModelType &modelType) {
+    switch(modelType) {
+      case(ModelType::MDP):
+        os << "mdp";
+        break;
+      case(ModelType::SMG):
+        os << "smg";
+        break;
+    }
+    os << "\n\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printBackgroundLabels(std::ostream &os, const AgentName &agentName, const std::pair<Color, cells> &backgroundTiles) {
+    if(backgroundTiles.second.size() == 0) return os;
+
+    bool first = true;
+    std::string color = getColor(backgroundTiles.first);
+    color.at(0) = std::toupper(color.at(0));
+    os << "label " << agentName << "On" << color << " = ";
+    for(auto const& cell : backgroundTiles.second) {
+      if(first) first = false; else os << " | ";
+      os << "(x" << agentName << "=" << cell.column << "&y" << agentName << "=" << cell.row << ")";
+    }
+    os << ";\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printRestrictionFormula(std::ostream& os, const AgentName &agentName, const std::string &direction,  const cells &cells) {
+    bool first = true;
+    os << "formula " << agentName << "CannotMove" << direction << " = " ;
+    for(auto const& cell : cells) {
+      if(first) first = false;
+      else os << " | ";
+      os << "(x" << agentName << "=" << cell.column << "&y" << agentName << "=" << cell.row << ")";
+    }
+    os << ";\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printIsOnSlipperyFormula(std::ostream& os, const AgentName &agentName, const std::vector<std::reference_wrapper<cells>>& slipperyCollection) {
+
+    if(std::find_if(slipperyCollection.cbegin(), slipperyCollection.cend(), [=](const std::reference_wrapper<cells>& c) { return !c.get().empty(); }) == slipperyCollection.cend()) {
+      os << "formula " << agentName << "IsOnSlippery = false;\n";
+      return os;
+    }
+
+    bool first = true;
+    os << "formula " << agentName << "IsOnSlippery = ";
+
+    for (const auto& slippery: slipperyCollection) {
+      for(const auto& cell : slippery.get()) {
+        if(first) first = false; else os << " | ";
+        os << "(x" << agentName << "=" << cell.column << "&y" << agentName << "=" << cell.row << ")";
+      }
+    }
+    os << ";\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printIsInLavaFormula(std::ostream& os, const AgentName &agentName, const cells &lava) {
+    if(lava.size() == 0) {
+      os << "formula " << agentName << "IsInLava = false;\n";
+      return os;
+    }
+    bool first = true;
+    os << "formula " << agentName << "IsInLava = ";
+    for(auto const& cell : lava) {
+      if(first) first = false; else os << " | ";
+      os << "(x" << agentName << "=" << cell.column << "&y" << agentName << "=" << cell.row << ")";
+    }
+    os << ";\n";
+    os << "formula " << agentName << "IsInLavaAndNotDone = " << agentName << "IsInLava & !" << agentName << "Done;\n";
+    os << "label \"" << agentName << "IsInLavaAndNotDone\" = " << agentName << "IsInLava & !" << agentName << "Done;\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printFormulas(std::ostream& os,
+                              const AgentName &agentName,
+                              const cells &restrictionNorth,
+                              const cells &restrictionEast,
+                              const cells &restrictionSouth,
+                              const cells &restrictionWest,
+                              const std::vector<std::reference_wrapper<cells>> &slipperyCollection,
+                              const cells &lava) {
+    printRestrictionFormula(os, agentName, "North", restrictionNorth);
+    printRestrictionFormula(os, agentName, "East ", restrictionEast);
+    printRestrictionFormula(os, agentName, "South", restrictionSouth);
+    printRestrictionFormula(os, agentName, "West ", restrictionWest);
+    printIsOnSlipperyFormula(os, agentName, slipperyCollection);
+    printIsInLavaFormula(os, agentName, lava);
+    os << "\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printGoalLabel(std::ostream& os, const AgentName &agentName, const cells &goals) {
+    if(goals.size() == 0) {
+      os << "formula " << agentName << "IsInGoal = false;\n";
+      return os;
+    }
+
+    bool first = true;
+    os << "formula " << agentName << "IsInGoal = ";
+    for(auto const& cell : goals) {
+      if(first) first = false; else os << " | ";
+      os << "(x" << agentName << "=" << cell.column << "&y" << agentName << "=" << cell.row << ")";
+    }
+    os << ";\n";
+    os << "formula " << agentName << "IsInGoalAndNotDone = " << agentName << "IsInGoal & !" << agentName << "Done;\n";
+    os << "label \"" << agentName << "IsInGoalAndNotDone\" = " << agentName << "IsInGoal & !" << agentName << "Done;\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printCrashLabel(std::ostream &os, const std::vector<AgentName> agentNames) {
+    os << "label crash = ";
+    bool first = true;
+    for(auto const& agentName : agentNames) {
+      if(agentName == "Agent") continue;
+      if(first) first = false; else os << " | ";
+      os << "(xAgent=x" << agentName << ")&(yAgent=y" << agentName << ")";
+    }
+    os << ";\n\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printAvoidanceLabel(std::ostream &os, const std::vector<AgentName> agentNames, const int &distance) {
+    os << "label avoidance = ";
+    bool first = true;
+    for(auto const& agentName : agentNames) {
+      if(agentName == "Agent") continue;
+      if(first) first = false; else os << " | ";
+      os << "max(xAgent-x" << agentName << ",x" << agentName << "-xAgent)+";
+      os << "max(yAgent-y" << agentName << ",y" << agentName << "-yAgent) ";
+    }
+    os << ";\n\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printKeysLabels(std::ostream& os, const AgentName &agentName, const cells &keys) {
+    if(keys.size() == 0) return os;
+
+    for(auto const& key : keys) {
+      std::string keyColor = key.getColor();
+      os << "label picked_up_" << keyColor << "_key = has_" << keyColor << "_key = true;\n";
+    }
+    os << "\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printBooleansForKeys(std::ostream &os, const cells &keys) {
+    for(auto const& key : keys) {
+      os << "\thas_"<< key.getColor() << "_key : bool init false;\n";
+    }
+    os << "\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printActionsForKeys(std::ostream &os, cells keys) {
+    for(auto const& key : keys) {
+      std::string keyColor = key.getColor();
+      os << "\t[pickup_" << keyColor << "_key] \n";
+      os << "\t\tview=0 & x=" << key.row     << " & y=" << key.column - 1 << " |\n";
+      os << "\t\tview=1 & x=" << key.row - 1 << " & y=" << key.column     << " |\n";
+      os << "\t\tview=2 & x=" << key.row     << " & y=" << key.column + 1 << " |\n";
+      os << "\t\tview=3 & x=" << key.row + 1 << " & y=" << key.column     << " -> (has_" << keyColor << "_key'=true);\n";
+    }
+    os << "\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printModule(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &boundaries, const coordinates& initialPosition, const cells &keys, const bool agentWithView, const std::vector<float> &probabilities) {
+    os << "module " << agentName << "\n";
+    os << "\tx" << agentName << " : [1.." << boundaries.first  << "] init " << initialPosition.second  << ";\n";
+    os << "\ty" << agentName << " : [1.." << boundaries.second << "] init " << initialPosition.first << ";\n";
+    printBooleansForKeys(os, keys);
+    os << "\t" << agentName << "Done : bool init false;\n";
+    if(agentWithView) {
+      os << "\tview" << agentName << " : [0..3] init 0;\n";
+    os << "\n";
+    os << "\t[" << agentName << "_turn_right] " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & !" << agentName << "IsOnSlippery -> (view" << agentName << "'=mod(view" << agentName << " + 1, 4)) " << moveUpdate(agentIndex) << ";\n";
+    os << "\t[" << agentName << "_turn_left]  " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & !" << agentName << "IsOnSlippery & view" << agentName << ">0 -> (view" << agentName << "'=view" << agentName << " - 1) " << moveUpdate(agentIndex) << ";\n";
+    os << "\t[" << agentName << "_turn_left]  " << moveGuard(agentIndex) << " !" << agentName << "IsInGoal & !" << agentName << "IsInLava & !" << agentName << "IsOnSlippery & view" << agentName << "=0 -> (view" << agentName << "'=3) " << moveUpdate(agentIndex) << ";\n";
+    } else {
+      os << "\t[" << agentName << "_turns]  " << moveGuard(agentIndex) << " true -> (x" << agentName << "'=x" << agentName << ")" << moveUpdate(agentIndex) << ";\n";
+    }
+    printActionsForKeys(os, keys);
+    os << "\n";
+    printMovementActions(os, agentName, agentIndex, agentWithView);
+    for(auto const& probability : probabilities) {
+      printMovementActions(os, agentName, agentIndex, agentWithView, probability);
+    }
+    printDoneActions(os, agentName, agentIndex);
+    os << "\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printMovementActions(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const bool agentWithView, const float &probability) {
+    if(probability >= 1) {
+      os << "\t[" << agentName << "_move_north]" << moveGuard(agentIndex) << viewVariable(agentName, 3, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava &!" << agentName << "IsInGoal &  !" << agentName << "CannotMoveNorth -> (y" << agentName << "'=y" << agentName << "-1)" << moveUpdate(agentIndex) << ";\n";
+      os << "\t[" << agentName << "_move_east] " << moveGuard(agentIndex) << viewVariable(agentName, 0, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava &!" << agentName << "IsInGoal &  !" << agentName << "CannotMoveEast  -> (x" << agentName << "'=x" << agentName << "+1)" << moveUpdate(agentIndex) << ";\n";
+      os << "\t[" << agentName << "_move_south]" << moveGuard(agentIndex) << viewVariable(agentName, 1, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava &!" << agentName << "IsInGoal &  !" << agentName << "CannotMoveSouth -> (y" << agentName << "'=y" << agentName << "+1)" << moveUpdate(agentIndex) << ";\n";
+      os << "\t[" << agentName << "_move_west] " << moveGuard(agentIndex) << viewVariable(agentName, 2, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava &!" << agentName << "IsInGoal &  !" << agentName << "CannotMoveWest  -> (x" << agentName << "'=x" << agentName << "-1)" << moveUpdate(agentIndex) << ";\n";
+    } else {
+      std::string probabilityString = std::to_string(probability);
+      std::string percentageString = std::to_string((int)(100 * probability));
+      std::string complementProbabilityString = std::to_string(1 - probability);
+      os << "\t[" << agentName << "_move_north_" << percentageString << "] ";
+      os << moveGuard(agentIndex) << viewVariable(agentName, 3, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava & !" << agentName << "CannotMoveNorth -> ";
+      os << probabilityString << ": (y" << agentName << "'=y" << agentName << "-1)" << moveUpdate(agentIndex) << " + ";
+      os << complementProbabilityString << ": (y" << agentName << "'=y" << agentName << ") " << moveUpdate(agentIndex) << ";\n";
+
+      os << "\t[" << agentName << "_move_east_" << percentageString << "] ";
+      os << moveGuard(agentIndex) << viewVariable(agentName, 0, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava & !" << agentName << "CannotMoveEast  -> ";
+      os << probabilityString << ": (x" << agentName << "'=x" << agentName << "+1)" << moveUpdate(agentIndex) << " + ";
+      os << complementProbabilityString << ": (x" << agentName << "'=x" << agentName << ") " << moveUpdate(agentIndex) << ";\n";
+
+      os << "\t[" << agentName << "_move_south_" << percentageString << "] ";
+      os << moveGuard(agentIndex) << viewVariable(agentName, 1, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava & !" << agentName << "CannotMoveSouth -> ";
+      os << probabilityString << ": (y" << agentName << "'=y" << agentName << "+1)" << moveUpdate(agentIndex) << " + ";
+      os << complementProbabilityString << ": (y" << agentName << "'=y" << agentName << ") " << moveUpdate(agentIndex) << ";\n";
+
+      os << "\t[" << agentName << "_move_west_" << percentageString << "] ";
+      os << moveGuard(agentIndex) << viewVariable(agentName, 2, agentWithView) << " !" << agentName << "IsOnSlippery & !" << agentName << "IsInLava & !" << agentName << "CannotMoveWest  -> ";
+      os << probabilityString << ": (x" << agentName << "'=x" << agentName << "-1)" << moveUpdate(agentIndex) << " + ";
+      os << complementProbabilityString << ": (x" << agentName << "'=x" << agentName << ") " << moveUpdate(agentIndex) << ";\n";
+    }
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printDoneActions(std::ostream &os, const AgentName &agentName, const size_t &agentIndex) {
+    os << "\t[" << agentName << "_done]" << moveGuard(agentIndex) << agentName << "IsInGoal | " << agentName << "IsInLava -> (" << agentName << "Done'=true);\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printSlipperyTurn(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &c, std::set<std::string> &slipperyActions, const std::array<bool, 8>& neighborhood, SlipperyType orientation) {
+    constexpr std::size_t PROB_PIECES = 9, ALL_POSS_DIRECTIONS = 9;
+
+    std::array<std::string, ALL_POSS_DIRECTIONS> positionTransition = {
+      /* north      */                                                "(y" + agentName + "'=y" + agentName + "-1)",
+      /* north east */   "(x" + agentName + "'=x" + agentName + "+1) & (y" + agentName + "'=y" + agentName + "-1)",
+      /* east       */   "(x" + agentName + "'=x" + agentName + "+1)",
+      /* east south */   "(x" + agentName + "'=x" + agentName + "+1) & (y" + agentName + "'=y" + agentName + "+1)",
+      /* south      */                                                "(y" + agentName + "'=y" + agentName + "+1)",
+      /* south west */   "(x" + agentName + "'=x" + agentName + "-1) & (y" + agentName + "'=y" + agentName + "+1)",
+      /* west       */   "(x" + agentName + "'=x" + agentName + "-1)",
+      /* north west */   "(x" + agentName + "'=x" + agentName + "-1) & (y" + agentName + "'=y" + agentName + "-1)",
+      /* own position */ "(x" + agentName + "'=x" + agentName + ") & (y" + agentName + "'=y" + agentName + ")"
+    };
+
+    // view transition appdx in form (guard, update part)
+    // IMPORTANT: No mod() usage for turn left due to bug in mod() function for decrement
+
+    std::array<std::pair<std::string, std::string>, 3> viewTransition = {
+        /* turn to right */ std::make_pair("",                           " & (view" + agentName + "'=mod(view" + agentName + " + 1, 4))"),
+        /* turn to left  */ std::make_pair(" & view" + agentName + ">0", " & (view" + agentName + "'=view" + agentName + " - 1)"),
+        /* turn to left  */ std::make_pair(" & view" + agentName + "=0", " & (view" + agentName + "'=3)")
+    };
+
+    // direction specifics
+
+    std::string actionName;
+    std::size_t remainPosIndex = 8;
+    std::array<std::size_t, ALL_POSS_DIRECTIONS> prob_piece_dir; // { n, ne, w, se, s, sw, w, nw, CURRENT POS }
+
+    switch (orientation)
+    {
+      case SlipperyType::North:
+        actionName = "\t[" + agentName + "turn_at_slip_north]";
+        prob_piece_dir = { 0, 0, 0, 1, 1, 1, 0, 0, 0 /* <- R */ };
+        break;
+
+      case SlipperyType::South:
+        actionName = "\t[" + agentName + "turn_at_slip_south]";
+        prob_piece_dir = { 1, 1, 0, 0, 0, 0, 0, 1, 0 /* <- R */ };
+        break;
+
+      case SlipperyType::East:
+        actionName = "\t[" + agentName + "turn_at_slip_east]";
+        prob_piece_dir = { 0, 0, 0, 0, 0, 1, 1, 1, 0 /* <- R */ };
+        break;
+
+      case SlipperyType::West:
+        actionName = "\t[" + agentName + "turn_at_slip_west]";
+        prob_piece_dir = { 0, 1, 1, 1, 0, 0, 0, 0, 0 /* <- R */ };
+        break;
+    }
+
+    slipperyActions.insert(actionName);
+
+    // override probability to 0 if corresp. direction is blocked
+
+    for (std::size_t i = 0; i < ALL_POSS_DIRECTIONS - 1; i++) {
+      if (!neighborhood.at(i))
+        prob_piece_dir.at(i) = 0;
+    }
+
+    // determine residual probability (R) by replacing 0 with (1 - overall sum)
+
+    prob_piece_dir.at(remainPosIndex) = PROB_PIECES - std::accumulate(prob_piece_dir.begin(), prob_piece_dir.end(), 0);
+
+    // <DEBUG_AREA>
+    {
+      assert(prob_piece_dir.at(remainPosIndex) <= 9 && prob_piece_dir.at(remainPosIndex) >= 6 && "Value not in Range!");
+      assert(std::accumulate(prob_piece_dir.begin(), prob_piece_dir.end(), 0) == PROB_PIECES && "Does not sum up to 1!");
+    }
+    // </DEBUG_AREA>
+
+
+    // generic output (for every view transition)
+    for (std::size_t v = 0; v < viewTransition.size(); v++) {
+
+        os << actionName << moveGuard(agentIndex) << " x" << agentName << "=" << c.second << " & y" << agentName << "=" << c.first << viewTransition.at(v).first;
+        for (std::size_t i = 0; i < ALL_POSS_DIRECTIONS; i++) {
+          os << (i == 0 ? " -> " : " + ") << prob_piece_dir.at(i) << "/" << PROB_PIECES << " : " << positionTransition.at(i) << viewTransition.at(v).second << moveUpdate(agentIndex) << (i == ALL_POSS_DIRECTIONS - 1 ? ";\n" : "\n");
+        }
+    }
+
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printSlipperyMove(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &c, std::set<std::string> &slipperyActions, const std::array<bool, 8>& neighborhood, SlipperyType orientation) {
+    constexpr std::size_t PROB_PIECES = 9, ALL_POSS_DIRECTIONS = 8;
+
+    std::array<std::string, ALL_POSS_DIRECTIONS> positionTransition = {
+      /* north      */                                              "(y" + agentName + "'=y" + agentName + "-1)",
+      /* north east */ "(x" + agentName + "'=x" + agentName + "+1) & (y" + agentName + "'=y" + agentName + "-1)",
+      /* east       */ "(x" + agentName + "'=x" + agentName + "+1)",
+      /* east south */ "(x" + agentName + "'=x" + agentName + "+1) & (y" + agentName + "'=y" + agentName + "+1)",
+      /* south      */                                              "(y" + agentName + "'=y" + agentName + "+1)",
+      /* south west */ "(x" + agentName + "'=x" + agentName + "-1) & (y" + agentName + "'=y" + agentName + "+1)",
+      /* west       */ "(x" + agentName + "'=x" + agentName + "-1)",
+      /* north west */ "(x" + agentName + "'=x" + agentName + "-1) & (y" + agentName + "'=y" + agentName + "-1)"
+    };
+
+    // direction specifics
+
+    std::size_t straightPosIndex;
+    std::string actionName, specialTransition; // if straight ahead is blocked
+    std::array<std::size_t, ALL_POSS_DIRECTIONS> prob_piece_dir; // { n, ne, w, se, s, sw, w, nw }
+
+    switch (orientation)
+    {
+      case SlipperyType::North:
+        actionName = "\t[" + agentName + "move_on_slip_north]";
+        prob_piece_dir = { 0, 0, 1, 2, 0 /* <- R */, 2, 1, 0 };
+        straightPosIndex = 4;
+        specialTransition = "(y" + agentName + "'=y" + agentName + (!neighborhood.at(straightPosIndex) ? ")" : "+1)");
+        break;
+
+      case SlipperyType::South:
+        actionName = "\t[" + agentName + "move_on_slip_south]";
+        prob_piece_dir = { 0 /* <- R */, 2, 1, 0, 0, 0, 1, 2 };
+        straightPosIndex = 0; // always north
+        specialTransition = "(y" + agentName + "'=y" + agentName + (!neighborhood.at(straightPosIndex) ? ")" : "-1)");
+        break;
+
+      case SlipperyType::East:
+        actionName = "\t[" + agentName + "move_on_slip_east]";
+        prob_piece_dir = { 1, 0, 0, 0, 1, 2, 0 /* <- R */, 2 };
+        straightPosIndex = 6;
+        specialTransition = "(x" + agentName + "'=x" + agentName + (!neighborhood.at(straightPosIndex) ? ")" : "-1)");
+        break;
+
+      case SlipperyType::West:
+        actionName = "\t[" + agentName + "move_on_slip_west]";
+        prob_piece_dir = { 1, 2, 0 /* <- R */, 2, 1, 0, 0, 0 };
+        straightPosIndex = 2;
+        specialTransition = "(x" + agentName + "'=x" + agentName + (!neighborhood.at(straightPosIndex) ? ")" : "+1)");
+        break;
+    }
+
+    slipperyActions.insert(actionName);
+
+    // override probability to 0 if corresp. direction is blocked
+
+    for (std::size_t i = 0; i < ALL_POSS_DIRECTIONS; i++) {
+      if (!neighborhood.at(i))
+        prob_piece_dir.at(i) = 0;
+    }
+
+    // determine residual probability (R) by replacing 0 with (1 - overall sum)
+
+    prob_piece_dir.at(straightPosIndex) = PROB_PIECES - std::accumulate(prob_piece_dir.begin(), prob_piece_dir.end(), 0);
+
+    // <DEBUG_AREA>
+    {
+      assert(prob_piece_dir.at(straightPosIndex) <= 9 && prob_piece_dir.at(straightPosIndex) >= 3 && "Value not in Range!");
+      assert(std::accumulate(prob_piece_dir.begin(), prob_piece_dir.end(), 0) == PROB_PIECES && "Does not sum up to 1!");
+      assert(orientation != SlipperyType::North || (prob_piece_dir.at(7) == 0 && prob_piece_dir.at(0) == 0 && prob_piece_dir.at(1) == 0 && "Slippery up should be impossible!"));
+      assert(orientation != SlipperyType::South || (prob_piece_dir.at(3) == 0 && prob_piece_dir.at(4) == 0 && prob_piece_dir.at(5) == 0 && "Slippery down should be impossible!"));
+      assert(orientation != SlipperyType::East  || (prob_piece_dir.at(1) == 0 && prob_piece_dir.at(2) == 0 && prob_piece_dir.at(3) == 0 && "Slippery right should be impossible!"));
+      assert(orientation != SlipperyType::West  || (prob_piece_dir.at(5) == 0 && prob_piece_dir.at(6) == 0 && prob_piece_dir.at(7) == 0 && "Slippery left should be impossible!"));
+    }
+    // </DEBUG_AREA>
+
+    // special case: straight forward is blocked (then remain in same position)
+
+    positionTransition.at(straightPosIndex) = specialTransition;
+
+    // generic output (for every view and every possible view direction)
+
+    os << actionName << moveGuard(agentIndex) << " x" << agentName << "=" << c.second << " & y" << agentName << "=" << c.first;
+
+    for (std::size_t i = 0; i < ALL_POSS_DIRECTIONS; i++) {
+      os << (i == 0 ? " -> " : " + ") << prob_piece_dir.at(i) << "/" << PROB_PIECES << " : " << positionTransition.at(i) << moveUpdate(agentIndex) << (i == ALL_POSS_DIRECTIONS - 1 ? ";\n" : "\n");
+    }
+
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printEndmodule(std::ostream &os) {
+    os << "endmodule\n";
+    os << "\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printPlayerStruct(std::ostream &os, const AgentName &agentName, const bool agentWithView, const std::vector<float> &probabilities, const std::set<std::string> &slipperyActions) {
+    os << "player " << agentName << "\n\t";
+    bool first = true;
+    std::list<std::string> allActions = { "_move_north", "_move_east", "_move_south", "_move_west" };
+    std::list<std::string> movementActions = allActions;
+    for(auto const& probability : probabilities) {
+      std::string percentageString = std::to_string((int)(100 * probability));
+      for(auto const& movement : movementActions) {
+        allActions.push_back(movement + "_" + percentageString);
+      }
+    }
+    if(agentWithView) {
+      allActions.push_back("_turn_left");
+      allActions.push_back("_turn_right");
+    } else {
+      allActions.push_back("_turns");
+    }
+
+    for(auto const& action : allActions) {
+      if(first) first = false; else os << ", ";
+      os << "[" << agentName << action << "]";
+    }
+    for(auto const& action : slipperyActions) {
+      os << ", " << action;
+    }
+    os << "\nendplayer\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printGlobalMoveVariable(std::ostream &os, const size_t &numberOfPlayer) {
+    os << "\nglobal move : [0.." << std::to_string(numberOfPlayer - 1) << "] init 0;\n\n";
+    return os;
+  }
+
+  std::ostream& PrismModulesPrinter::printRewards(std::ostream &os, const std::map<coordinates, float> &stateRewards, const cells &lava, const cells &goals) {
+    os << "rewards \"NoBFS\"\n";
+    os << "\tAgentIsInGoalAndNotDone:  100;\n";
+    os << "\tAgentIsInLavaAndNotDone: -100;\n";
+    os << "endrewards\n";
+    os << "rewards \"WithBFS\"\n";
+    os << "\tAgentIsInGoalAndNotDone:  100;\n";
+    os << "\tAgentIsInLavaAndNotDone: -100;\n";
+    for(auto const [coordinates, reward] : stateRewards) {
+      os << "\txAgent=" << coordinates.first << "&yAgent=" << coordinates.second << " : " << reward << ";\n";
+    }
+    os << "endrewards\n";
+    return os;
+  }
+
+  std::string PrismModulesPrinter::moveGuard(const size_t &agentIndex) {
+    return isGame() ? " move=" + std::to_string(agentIndex) + " & " : " ";
+  }
+
+  std::string PrismModulesPrinter::moveUpdate(const size_t &agentIndex) {
+    return isGame() ?
+             (agentIndex == numberOfPlayer - 1) ?
+               " & (move'=0) " :
+               " & (move'=" + std::to_string(agentIndex + 1) + ") " :
+               "";
+  }
+
+  std::string PrismModulesPrinter::viewVariable(const AgentName &agentName, const size_t &agentDirection, const bool agentWithView) {
+    return agentWithView ? " view" + agentName + "=" + std::to_string(agentDirection) + " & " : " ";
+  }
+
+  bool PrismModulesPrinter::isGame() const {
+    return modelType == ModelType::SMG;
+  }
+}

Minigrid2PRISM/util/PrismModulesPrinter.h

@@ -0,0 +1,78 @@
+#pragma once
+
+#include <iostream>
+#include <functional>
+#include "MinigridGrammar.h"
+#include "PrismPrinter.h"
+
+namespace prism {
+  class PrismModulesPrinter {
+    public:
+      PrismModulesPrinter(const ModelType &modelType, const size_t &numberOfPlayer);
+
+      std::ostream& printRestrictionFormula(std::ostream& os, const AgentName &agentName, const std::string &direction, const cells &cells);
+      std::ostream& printIsOnSlipperyFormula(std::ostream& os, const AgentName &agentName, const std::vector<std::reference_wrapper<cells>> &slipperyCollection);
+      std::ostream& printGoalLabel(std::ostream& os, const AgentName&agentName, const cells &goals);
+      std::ostream& printCrashLabel(std::ostream &os, const std::vector<AgentName> agentNames);
+      std::ostream& printAvoidanceLabel(std::ostream &os, const std::vector<AgentName> agentNames, const int &distance);
+      std::ostream& printKeysLabels(std::ostream& os, const AgentName&agentName, const cells &keys);
+      std::ostream& printBackgroundLabels(std::ostream &os, const AgentName &agentName, const std::pair<Color, cells> &backgroundTiles);
+      std::ostream& printIsInLavaFormula(std::ostream& os, const AgentName &agentName, const cells &lava);
+      std::ostream& printFormulas(std::ostream& os,
+                                  const AgentName&agentName,
+                                  const cells &restrictionNorth,
+                                  const cells &restrictionEast,
+                                  const cells &restrictionSouth,
+                                  const cells &restrictionWest,
+                                  const std::vector<std::reference_wrapper<cells>> &slipperyCollection,
+                                  const cells &lava);
+
+       /*
+        * Representation for Slippery Tile. 
+        *  -) North: Slips from North to South
+        *  -) East: Slips from East to West
+        *  -) South: Slips from South to North
+        *  -) West: Slips from West to East
+        */
+      enum class SlipperyType { North, East, South, West }; 
+
+      /* 
+       * Prints Slippery on move action. 
+       * 
+       * @param neighborhood: Information of wall-blocks in 8-neighborhood { n, nw, e, se, s, sw, w, nw }. If entry is false, then corresponding neighboorhood position is a wall.
+       * @param orientation: Information of slippery type (either north, south, east, west).
+       */
+      std::ostream& printSlipperyMove(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &c, std::set<std::string> &slipperyActions, const std::array<bool, 8>& neighborhood, SlipperyType orientation);
+      
+      /* 
+       * Prints Slippery on turn action. 
+       *   
+       * @param neighborhood: Information of wall-blocks in 8-neighborhood { n, nw, e, se, s, sw, w, nw }. If entry is false, then corresponding neighboorhood position is a wall.
+       * @param orientation: Information of slippery type (either north, south, east, west).
+       */
+      std::ostream& printSlipperyTurn(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &c, std::set<std::string> &slipperyActions, const std::array<bool, 8>& neighborhood, SlipperyType orientation);
+      
+      std::ostream& printModel(std::ostream &os, const ModelType &modelType);
+      std::ostream& printBooleansForKeys(std::ostream &os, const cells &keys);
+      std::ostream& printActionsForKeys(std::ostream &os, cells keys);
+      std::ostream& printModule(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const coordinates &boundaries, const coordinates& initialPosition, const cells &keys, const bool agentWithView, const std::vector<float> &probabilities = {});
+      std::ostream& printMovementActions(std::ostream &os, const AgentName &agentName, const size_t &agentIndex, const bool agentWithView, const float &probability = 1.0);
+      std::ostream& printDoneActions(std::ostream &os, const AgentName &agentName, const size_t &agentIndex);
+      std::ostream& printEndmodule(std::ostream &os);
+      std::ostream& printPlayerStruct(std::ostream &os, const AgentName &agentName, const bool agentWithView, const std::vector<float> &probabilities = {}, const std::set<std::string> &slipperyActions = {});
+      std::ostream& printGlobalMoveVariable(std::ostream &os, const size_t &numberOfPlayer);
+
+      std::ostream& printRewards(std::ostream &os, const std::map<coordinates, float> &stateRewards, const cells &lava, const cells &goals);
+
+      std::string moveGuard(const size_t &agentIndex);
+      std::string moveUpdate(const size_t &agentIndex);
+
+      std::string viewVariable(const AgentName &agentName, const size_t &agentDirection, const bool agentWithView);
+
+      bool isGame() const;
+    private:
+      
+      ModelType const& modelType;
+      const size_t numberOfPlayer;
+  };
+}

Minigrid2PRISM/util/PrismPrinter.h

@@ -0,0 +1,15 @@
+#pragma once
+
+#include <string>
+
+#include "cell.h"
+
+typedef std::string AgentName;
+typedef std::pair<std::string, coordinates> AgentNameAndPosition;
+typedef std::map<AgentNameAndPosition::first_type, AgentNameAndPosition::second_type> AgentNameAndPositionMap;
+
+namespace prism {
+  enum class ModelType {
+    MDP, SMG
+  };
+}

Minigrid2PRISM/util/cell.cpp

@@ -0,0 +1,83 @@
+#include "cell.h"
+
+#include <stdexcept>
+
+std::ostream &operator<<(std::ostream &os, const cell &c) {
+  os << static_cast<char>(c.type) << static_cast<char>(c.color);
+  os <<  " at (" << c.row << "," << c.column << ")";
+  return os;
+}
+
+cell cell::getNorth(const std::vector<cell> &grid) const {
+  auto north = std::find_if(grid.begin(), grid.end(), [this](const cell &c) {
+        return this->row - 1 == c.row && this->column == c.column;
+      });
+  if(north == grid.end()) {
+    throw std::logic_error{ "Cannot get cell north of (" + std::to_string(row) + "," + std::to_string(column) + ")"};
+    std::exit(EXIT_FAILURE);
+  }
+  return *north;
+}
+
+cell cell::getEast(const std::vector<cell> &grid) const {
+  auto east = std::find_if(grid.begin(), grid.end(), [this](const cell &c) {
+        return this->row == c.row && this->column + 1 == c.column;
+      });
+  if(east == grid.end()) {
+    throw std::logic_error{ "Cannot get cell east of (" + std::to_string(row) + "," + std::to_string(column) + ")"};
+    std::exit(EXIT_FAILURE);
+  }
+  return *east;
+}
+
+cell cell::getSouth(const std::vector<cell> &grid) const {
+  auto south = std::find_if(grid.begin(), grid.end(), [this](const cell &c) {
+        return this->row + 1 == c.row && this->column == c.column;
+      });
+  if(south == grid.end()) {
+    throw std::logic_error{ "Cannot get cell south of (" + std::to_string(row) + "," + std::to_string(column) + ")"};
+    std::exit(EXIT_FAILURE);
+  }
+  return *south;
+}
+
+cell cell::getWest(const std::vector<cell> &grid) const {
+  auto west = std::find_if(grid.begin(), grid.end(), [this](const cell &c) {
+        return this->row == c.row && this->column - 1 == c.column;
+      });
+  if(west == grid.end()) {
+    throw std::logic_error{ "Cannot get cell west of (" + std::to_string(row) + "," + std::to_string(column) + ")"};
+    std::exit(EXIT_FAILURE);
+  }
+  return *west;
+}
+
+coordinates cell::getCoordinates() const {
+  return std::make_pair(row, column);
+}
+
+std::string cell::getColor() const {
+  switch(color) {
+    case Color::Red:    return "red";
+    case Color::Green:  return "green";
+    case Color::Blue:   return "blue";
+    case Color::Purple: return "purple";
+    case Color::Yellow: return "yellow";
+    case Color::None:   return "transparent";
+    default: return "";
+    //case Color::Grey   = 'G',
+  }
+}
+
+std::string getColor(Color color) {
+  switch(color) {
+    case Color::Red:    return "red";
+    case Color::Green:  return "green";
+    case Color::Blue:   return "blue";
+    case Color::Purple: return "purple";
+    case Color::Yellow: return "yellow";
+    case Color::None:   return "transparent";
+    default: return "";
+    //case Color::Grey   = 'G',
+  }
+}

Minigrid2PRISM/util/cell.h

@@ -0,0 +1,62 @@
+#pragma once
+
+#include <iostream>
+#include <utility>
+#include <vector>
+
+typedef std::pair<int, int> coordinates;
+
+class Grid;
+
+enum class Type : char {
+  Wall       = 'W',
+  Floor      = ' ',
+  Door       = 'D',
+  LockedDoor = 'L',
+  Key        = 'K',
+  Ball       = 'A',
+  Box        = 'B',
+  Goal       = 'G',
+  Lava       = 'V',
+  Agent      = 'X',
+  Adversary  = 'Z',
+  SlipperyNorth = 'n',
+  SlipperySouth = 's',
+  SlipperyEast  = 'e',
+  SlipperyWest  = 'w'
+};
+enum class Color : char {
+  Red    = 'R',
+  Green  = 'G',
+  Blue   = 'B',
+  Purple = 'P',
+  Yellow = 'Y',
+  //Grey   = 'G',
+  None   = ' '
+};
+
+constexpr std::initializer_list<Color> allColors = {Color::Red, Color::Green, Color::Blue, Color::Purple, Color::Yellow};
+std::string getColor(Color color);
+
+class cell {
+  public:
+    coordinates getNorth() const { return std::make_pair(row - 1, column); }
+    coordinates getSouth() const { return std::make_pair(row + 1, column); }
+    coordinates getEast()  const { return std::make_pair(row, column + 1); }
+    coordinates getWest()  const { return std::make_pair(row, column - 1); }
+
+    cell getNorth(const std::vector<cell> &grid) const;
+    cell getEast(const std::vector<cell> &grid) const;
+    cell getSouth(const std::vector<cell> &grid) const;
+    cell getWest(const std::vector<cell> &grid) const;
+
+    friend std::ostream& operator<<(std::ostream& os, const cell& cell);
+
+    coordinates getCoordinates() const;
+    std::string getColor() const;
+
+    int row;
+    int column;
+    Type type;
+    Color color;
+};

gym_minigrid/.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

gym_minigrid/.orig_git/HEAD

@@ -0,0 +1 @@
+ref: refs/heads/ag_proof_of_concept

gym_minigrid/.orig_git/config

@@ -0,0 +1,11 @@
+[core]
+	repositoryformatversion = 0
+	filemode = true
+	bare = false
+	logallrefupdates = true
+[remote "origin"]
+	url = gitea@git.pranger.xyz:sp/Minigrid.git
+	fetch = +refs/heads/*:refs/remotes/origin/*
+[branch "ag_proof_of_concept"]
+	remote = origin
+	merge = refs/heads/ag_proof_of_concept

gym_minigrid/.orig_git/description

@@ -0,0 +1 @@
+Unnamed repository; edit this file 'description' to name the repository.

gym_minigrid/.orig_git/hooks/applypatch-msg.sample

@@ -0,0 +1,15 @@
+#!/bin/sh
+#
+# An example hook script to check the commit log message taken by
+# applypatch from an e-mail message.
+#
+# The hook should exit with non-zero status after issuing an
+# appropriate message if it wants to stop the commit.  The hook is
+# allowed to edit the commit message file.
+#
+# To enable this hook, rename this file to "applypatch-msg".
+
+. git-sh-setup
+commitmsg="$(git rev-parse --git-path hooks/commit-msg)"
+test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"}
+:

gym_minigrid/.orig_git/hooks/commit-msg.sample

@@ -0,0 +1,24 @@
+#!/bin/sh
+#
+# An example hook script to check the commit log message.
+# Called by "git commit" with one argument, the name of the file
+# that has the commit message.  The hook should exit with non-zero
+# status after issuing an appropriate message if it wants to stop the
+# commit.  The hook is allowed to edit the commit message file.
+#
+# To enable this hook, rename this file to "commit-msg".
+
+# Uncomment the below to add a Signed-off-by line to the message.
+# Doing this in a hook is a bad idea in general, but the prepare-commit-msg
+# hook is more suited to it.
+#
+# SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p')
+# grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1"
+
+# This example catches duplicate Signed-off-by lines.
+
+test "" = "$(grep '^Signed-off-by: ' "$1" |
+	 sort | uniq -c | sed -e '/^[ 	]*1[ 	]/d')" || {
+	echo >&2 Duplicate Signed-off-by lines.
+	exit 1
+}

gym_minigrid/.orig_git/hooks/fsmonitor-watchman.sample

@@ -0,0 +1,109 @@
+#!/usr/bin/perl
+
+use strict;
+use warnings;
+use IPC::Open2;
+
+# An example hook script to integrate Watchman
+# (https://facebook.github.io/watchman/) with git to speed up detecting
+# new and modified files.
+#
+# The hook is passed a version (currently 1) and a time in nanoseconds
+# formatted as a string and outputs to stdout all files that have been
+# modified since the given time. Paths must be relative to the root of
+# the working tree and separated by a single NUL.
+#
+# To enable this hook, rename this file to "query-watchman" and set
+# 'git config core.fsmonitor .git/hooks/query-watchman'
+#
+my ($version, $time) = @ARGV;
+
+# Check the hook interface version
+
+if ($version == 1) {
+	# convert nanoseconds to seconds
+	# subtract one second to make sure watchman will return all changes
+	$time = int ($time / 1000000000) - 1;
+} else {
+	die "Unsupported query-fsmonitor hook version '$version'.\n" .
+	    "Falling back to scanning...\n";
+}
+
+my $git_work_tree;
+if ($^O =~ 'msys' || $^O =~ 'cygwin') {
+	$git_work_tree = Win32::GetCwd();
+	$git_work_tree =~ tr/\\/\//;
+} else {
+	require Cwd;
+	$git_work_tree = Cwd::cwd();
+}
+
+my $retry = 1;
+
+launch_watchman();
+
+sub launch_watchman {
+
+	my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty')
+	    or die "open2() failed: $!\n" .
+	    "Falling back to scanning...\n";
+
+	# In the query expression below we're asking for names of files that
+	# changed since $time but were not transient (ie created after
+	# $time but no longer exist).
+	#
+	# To accomplish this, we're using the "since" generator to use the
+	# recency index to select candidate nodes and "fields" to limit the
+	# output to file names only.
+
+	my $query = <<"	END";
+		["query", "$git_work_tree", {
+			"since": $time,
+			"fields": ["name"]
+		}]
+	END
+
+	print CHLD_IN $query;
+	close CHLD_IN;
+	my $response = do {local $/; <CHLD_OUT>};
+
+	die "Watchman: command returned no output.\n" .
+	    "Falling back to scanning...\n" if $response eq "";
+	die "Watchman: command returned invalid output: $response\n" .
+	    "Falling back to scanning...\n" unless $response =~ /^\{/;
+
+	my $json_pkg;
+	eval {
+		require JSON::XS;
+		$json_pkg = "JSON::XS";
+		1;
+	} or do {
+		require JSON::PP;
+		$json_pkg = "JSON::PP";
+	};
+
+	my $o = $json_pkg->new->utf8->decode($response);
+
+	if ($retry > 0 and $o->{error} and $o->{error} =~ m/unable to resolve root .* directory (.*) is not watched/) {
+		print STDERR "Adding '$git_work_tree' to watchman's watch list.\n";
+		$retry--;
+		qx/watchman watch "$git_work_tree"/;
+		die "Failed to make watchman watch '$git_work_tree'.\n" .
+		    "Falling back to scanning...\n" if $? != 0;
+
+		# Watchman will always return all files on the first query so
+		# return the fast "everything is dirty" flag to git and do the
+		# Watchman query just to get it over with now so we won't pay
+		# the cost in git to look up each individual file.
+		print "/\0";
+		eval { launch_watchman() };
+		exit 0;
+	}
+
+	die "Watchman: $o->{error}.\n" .
+	    "Falling back to scanning...\n" if $o->{error};
+
+	binmode STDOUT, ":utf8";
+	local $, = "\0";
+	print @{$o->{files}};
+}

gym_minigrid/.orig_git/hooks/post-update.sample

@@ -0,0 +1,8 @@
+#!/bin/sh
+#
+# An example hook script to prepare a packed repository for use over
+# dumb transports.
+#
+# To enable this hook, rename this file to "post-update".
+
+exec git update-server-info

gym_minigrid/.orig_git/hooks/pre-applypatch.sample

@@ -0,0 +1,14 @@
+#!/bin/sh
+#
+# An example hook script to verify what is about to be committed
+# by applypatch from an e-mail message.
+#
+# The hook should exit with non-zero status after issuing an
+# appropriate message if it wants to stop the commit.
+#
+# To enable this hook, rename this file to "pre-applypatch".
+
+. git-sh-setup
+precommit="$(git rev-parse --git-path hooks/pre-commit)"
+test -x "$precommit" && exec "$precommit" ${1+"$@"}
+:

gym_minigrid/.orig_git/hooks/pre-commit.sample

@@ -0,0 +1,49 @@
+#!/bin/sh
+#
+# An example hook script to verify what is about to be committed.
+# Called by "git commit" with no arguments.  The hook should
+# exit with non-zero status after issuing an appropriate message if
+# it wants to stop the commit.
+#
+# To enable this hook, rename this file to "pre-commit".
+
+if git rev-parse --verify HEAD >/dev/null 2>&1
+then
+	against=HEAD
+else
+	# Initial commit: diff against an empty tree object
+	against=$(git hash-object -t tree /dev/null)
+fi
+
+# If you want to allow non-ASCII filenames set this variable to true.
+allownonascii=$(git config --bool hooks.allownonascii)
+
+# Redirect output to stderr.
+exec 1>&2
+
+# Cross platform projects tend to avoid non-ASCII filenames; prevent
+# them from being added to the repository. We exploit the fact that the
+# printable range starts at the space character and ends with tilde.
+if [ "$allownonascii" != "true" ] &&
+	# Note that the use of brackets around a tr range is ok here, (it's
+	# even required, for portability to Solaris 10's /usr/bin/tr), since
+	# the square bracket bytes happen to fall in the designated range.
+	test $(git diff --cached --name-only --diff-filter=A -z $against |
+	  LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0
+then
+	cat <<\EOF
+Error: Attempt to add a non-ASCII file name.
+
+This can cause problems if you want to work with people on other platforms.
+
+To be portable it is advisable to rename the file.
+
+If you know what you are doing you can disable this check using:
+
+  git config hooks.allownonascii true
+EOF
+	exit 1
+fi
+
+# If there are whitespace errors, print the offending file names and fail.
+exec git diff-index --check --cached $against --

gym_minigrid/.orig_git/hooks/pre-merge-commit.sample

@@ -0,0 +1,13 @@
+#!/bin/sh
+#
+# An example hook script to verify what is about to be committed.
+# Called by "git merge" with no arguments.  The hook should
+# exit with non-zero status after issuing an appropriate message to
+# stderr if it wants to stop the merge commit.
+#
+# To enable this hook, rename this file to "pre-merge-commit".
+
+. git-sh-setup
+test -x "$GIT_DIR/hooks/pre-commit" &&
+        exec "$GIT_DIR/hooks/pre-commit"
+:

gym_minigrid/.orig_git/hooks/pre-push.sample

@@ -0,0 +1,53 @@
+#!/bin/sh
+
+# An example hook script to verify what is about to be pushed.  Called by "git
+# push" after it has checked the remote status, but before anything has been
+# pushed.  If this script exits with a non-zero status nothing will be pushed.
+#
+# This hook is called with the following parameters:
+#
+# $1 -- Name of the remote to which the push is being done
+# $2 -- URL to which the push is being done
+#
+# If pushing without using a named remote those arguments will be equal.
+#
+# Information about the commits which are being pushed is supplied as lines to
+# the standard input in the form:
+#
+#   <local ref> <local sha1> <remote ref> <remote sha1>
+#
+# This sample shows how to prevent push of commits where the log message starts
+# with "WIP" (work in progress).
+
+remote="$1"
+url="$2"
+
+z40=0000000000000000000000000000000000000000
+
+while read local_ref local_sha remote_ref remote_sha
+do
+	if [ "$local_sha" = $z40 ]
+	then
+		# Handle delete
+		:
+	else
+		if [ "$remote_sha" = $z40 ]
+		then
+			# New branch, examine all commits
+			range="$local_sha"
+		else
+			# Update to existing branch, examine new commits
+			range="$remote_sha..$local_sha"
+		fi
+
+		# Check for WIP commit
+		commit=`git rev-list -n 1 --grep '^WIP' "$range"`
+		if [ -n "$commit" ]
+		then
+			echo >&2 "Found WIP commit in $local_ref, not pushing"
+			exit 1
+		fi
+	fi
+done
+
+exit 0

gym_minigrid/.orig_git/hooks/pre-rebase.sample

@@ -0,0 +1,169 @@
+#!/bin/sh
+#
+# Copyright (c) 2006, 2008 Junio C Hamano
+#
+# The "pre-rebase" hook is run just before "git rebase" starts doing
+# its job, and can prevent the command from running by exiting with
+# non-zero status.
+#
+# The hook is called with the following parameters:
+#
+# $1 -- the upstream the series was forked from.
+# $2 -- the branch being rebased (or empty when rebasing the current branch).
+#
+# This sample shows how to prevent topic branches that are already
+# merged to 'next' branch from getting rebased, because allowing it
+# would result in rebasing already published history.
+
+publish=next
+basebranch="$1"
+if test "$#" = 2
+then
+	topic="refs/heads/$2"
+else
+	topic=`git symbolic-ref HEAD` ||
+	exit 0 ;# we do not interrupt rebasing detached HEAD
+fi
+
+case "$topic" in
+refs/heads/??/*)
+	;;
+*)
+	exit 0 ;# we do not interrupt others.
+	;;
+esac
+
+# Now we are dealing with a topic branch being rebased
+# on top of master.  Is it OK to rebase it?
+
+# Does the topic really exist?
+git show-ref -q "$topic" || {
+	echo >&2 "No such branch $topic"
+	exit 1
+}
+
+# Is topic fully merged to master?
+not_in_master=`git rev-list --pretty=oneline ^master "$topic"`
+if test -z "$not_in_master"
+then
+	echo >&2 "$topic is fully merged to master; better remove it."
+	exit 1 ;# we could allow it, but there is no point.
+fi
+
+# Is topic ever merged to next?  If so you should not be rebasing it.
+only_next_1=`git rev-list ^master "^$topic" ${publish} | sort`
+only_next_2=`git rev-list ^master           ${publish} | sort`
+if test "$only_next_1" = "$only_next_2"
+then
+	not_in_topic=`git rev-list "^$topic" master`
+	if test -z "$not_in_topic"
+	then
+		echo >&2 "$topic is already up to date with master"
+		exit 1 ;# we could allow it, but there is no point.
+	else
+		exit 0
+	fi
+else
+	not_in_next=`git rev-list --pretty=oneline ^${publish} "$topic"`
+	/usr/bin/perl -e '
+		my $topic = $ARGV[0];
+		my $msg = "* $topic has commits already merged to public branch:\n";
+		my (%not_in_next) = map {
+			/^([0-9a-f]+) /;
+			($1 => 1);
+		} split(/\n/, $ARGV[1]);
+		for my $elem (map {
+				/^([0-9a-f]+) (.*)$/;
+				[$1 => $2];
+			} split(/\n/, $ARGV[2])) {
+			if (!exists $not_in_next{$elem->[0]}) {
+				if ($msg) {
+					print STDERR $msg;
+					undef $msg;
+				}
+				print STDERR " $elem->[1]\n";
+			}
+		}
+	' "$topic" "$not_in_next" "$not_in_master"
+	exit 1
+fi
+
+<<\DOC_END
+
+This sample hook safeguards topic branches that have been
+published from being rewound.
+
+The workflow assumed here is:
+
+ * Once a topic branch forks from "master", "master" is never
+   merged into it again (either directly or indirectly).
+
+ * Once a topic branch is fully cooked and merged into "master",
+   it is deleted.  If you need to build on top of it to correct
+   earlier mistakes, a new topic branch is created by forking at
+   the tip of the "master".  This is not strictly necessary, but
+   it makes it easier to keep your history simple.
+
+ * Whenever you need to test or publish your changes to topic
+   branches, merge them into "next" branch.
+
+The script, being an example, hardcodes the publish branch name
+to be "next", but it is trivial to make it configurable via
+$GIT_DIR/config mechanism.
+
+With this workflow, you would want to know:
+
+(1) ... if a topic branch has ever been merged to "next".  Young
+    topic branches can have stupid mistakes you would rather
+    clean up before publishing, and things that have not been
+    merged into other branches can be easily rebased without
+    affecting other people.  But once it is published, you would
+    not want to rewind it.
+
+(2) ... if a topic branch has been fully merged to "master".
+    Then you can delete it.  More importantly, you should not
+    build on top of it -- other people may already want to
+    change things related to the topic as patches against your
+    "master", so if you need further changes, it is better to
+    fork the topic (perhaps with the same name) afresh from the
+    tip of "master".
+
+Let's look at this example:
+
+		   o---o---o---o---o---o---o---o---o---o "next"
+		  /       /           /           /
+		 /   a---a---b A     /           /
+		/   /               /           /
+	       /   /   c---c---c---c B         /
+	      /   /   /             \         /
+	     /   /   /   b---b C     \       /
+	    /   /   /   /             \     /
+    ---o---o---o---o---o---o---o---o---o---o---o "master"
+
+
+A, B and C are topic branches.
+
+ * A has one fix since it was merged up to "next".
+
+ * B has finished.  It has been fully merged up to "master" and "next",
+   and is ready to be deleted.
+
+ * C has not merged to "next" at all.
+
+We would want to allow C to be rebased, refuse A, and encourage
+B to be deleted.
+
+To compute (1):
+
+	git rev-list ^master ^topic next
+	git rev-list ^master        next
+
+	if these match, topic has not merged in next at all.
+
+To compute (2):
+
+	git rev-list master..topic
+
+	if this is empty, it is fully merged to "master".
+
+DOC_END

gym_minigrid/.orig_git/hooks/pre-receive.sample

@@ -0,0 +1,24 @@
+#!/bin/sh
+#
+# An example hook script to make use of push options.
+# The example simply echoes all push options that start with 'echoback='
+# and rejects all pushes when the "reject" push option is used.
+#
+# To enable this hook, rename this file to "pre-receive".
+
+if test -n "$GIT_PUSH_OPTION_COUNT"
+then
+	i=0
+	while test "$i" -lt "$GIT_PUSH_OPTION_COUNT"
+	do
+		eval "value=\$GIT_PUSH_OPTION_$i"
+		case "$value" in
+		echoback=*)
+			echo "echo from the pre-receive-hook: ${value#*=}" >&2
+			;;
+		reject)
+			exit 1
+		esac
+		i=$((i + 1))
+	done
+fi

gym_minigrid/.orig_git/hooks/prepare-commit-msg.sample

@@ -0,0 +1,42 @@
+#!/bin/sh
+#
+# An example hook script to prepare the commit log message.
+# Called by "git commit" with the name of the file that has the
+# commit message, followed by the description of the commit
+# message's source.  The hook's purpose is to edit the commit
+# message file.  If the hook fails with a non-zero status,
+# the commit is aborted.
+#
+# To enable this hook, rename this file to "prepare-commit-msg".
+
+# This hook includes three examples. The first one removes the
+# "# Please enter the commit message..." help message.
+#
+# The second includes the output of "git diff --name-status -r"
+# into the message, just before the "git status" output.  It is
+# commented because it doesn't cope with --amend or with squashed
+# commits.
+#
+# The third example adds a Signed-off-by line to the message, that can
+# still be edited.  This is rarely a good idea.
+
+COMMIT_MSG_FILE=$1
+COMMIT_SOURCE=$2
+SHA1=$3
+
+/usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE"
+
+# case "$COMMIT_SOURCE,$SHA1" in
+#  ,|template,)
+#    /usr/bin/perl -i.bak -pe '
+#       print "\n" . `git diff --cached --name-status -r`
+# 	 if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;;
+#  *) ;;
+# esac
+
+# SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p')
+# git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE"
+# if test -z "$COMMIT_SOURCE"
+# then
+#   /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE"
+# fi

gym_minigrid/.orig_git/hooks/update.sample

@@ -0,0 +1,128 @@
+#!/bin/sh
+#
+# An example hook script to block unannotated tags from entering.
+# Called by "git receive-pack" with arguments: refname sha1-old sha1-new
+#
+# To enable this hook, rename this file to "update".
+#
+# Config
+# ------
+# hooks.allowunannotated
+#   This boolean sets whether unannotated tags will be allowed into the
+#   repository.  By default they won't be.
+# hooks.allowdeletetag
+#   This boolean sets whether deleting tags will be allowed in the
+#   repository.  By default they won't be.
+# hooks.allowmodifytag
+#   This boolean sets whether a tag may be modified after creation. By default
+#   it won't be.
+# hooks.allowdeletebranch
+#   This boolean sets whether deleting branches will be allowed in the
+#   repository.  By default they won't be.
+# hooks.denycreatebranch
+#   This boolean sets whether remotely creating branches will be denied
+#   in the repository.  By default this is allowed.
+#
+
+# --- Command line
+refname="$1"
+oldrev="$2"
+newrev="$3"
+
+# --- Safety check
+if [ -z "$GIT_DIR" ]; then
+	echo "Don't run this script from the command line." >&2
+	echo " (if you want, you could supply GIT_DIR then run" >&2
+	echo "  $0 <ref> <oldrev> <newrev>)" >&2
+	exit 1
+fi
+
+if [ -z "$refname" -o -z "$oldrev" -o -z "$newrev" ]; then
+	echo "usage: $0 <ref> <oldrev> <newrev>" >&2
+	exit 1
+fi
+
+# --- Config
+allowunannotated=$(git config --bool hooks.allowunannotated)
+allowdeletebranch=$(git config --bool hooks.allowdeletebranch)
+denycreatebranch=$(git config --bool hooks.denycreatebranch)
+allowdeletetag=$(git config --bool hooks.allowdeletetag)
+allowmodifytag=$(git config --bool hooks.allowmodifytag)
+
+# check for no description
+projectdesc=$(sed -e '1q' "$GIT_DIR/description")
+case "$projectdesc" in
+"Unnamed repository"* | "")
+	echo "*** Project description file hasn't been set" >&2
+	exit 1
+	;;
+esac
+
+# --- Check types
+# if $newrev is 0000...0000, it's a commit to delete a ref.
+zero="0000000000000000000000000000000000000000"
+if [ "$newrev" = "$zero" ]; then
+	newrev_type=delete
+else
+	newrev_type=$(git cat-file -t $newrev)
+fi
+
+case "$refname","$newrev_type" in
+	refs/tags/*,commit)
+		# un-annotated tag
+		short_refname=${refname##refs/tags/}
+		if [ "$allowunannotated" != "true" ]; then
+			echo "*** The un-annotated tag, $short_refname, is not allowed in this repository" >&2
+			echo "*** Use 'git tag [ -a | -s ]' for tags you want to propagate." >&2
+			exit 1
+		fi
+		;;
+	refs/tags/*,delete)
+		# delete tag
+		if [ "$allowdeletetag" != "true" ]; then
+			echo "*** Deleting a tag is not allowed in this repository" >&2
+			exit 1
+		fi
+		;;
+	refs/tags/*,tag)
+		# annotated tag
+		if [ "$allowmodifytag" != "true" ] && git rev-parse $refname > /dev/null 2>&1
+		then
+			echo "*** Tag '$refname' already exists." >&2
+			echo "*** Modifying a tag is not allowed in this repository." >&2
+			exit 1
+		fi
+		;;
+	refs/heads/*,commit)
+		# branch
+		if [ "$oldrev" = "$zero" -a "$denycreatebranch" = "true" ]; then
+			echo "*** Creating a branch is not allowed in this repository" >&2
+			exit 1
+		fi
+		;;
+	refs/heads/*,delete)
+		# delete branch
+		if [ "$allowdeletebranch" != "true" ]; then
+			echo "*** Deleting a branch is not allowed in this repository" >&2
+			exit 1
+		fi
+		;;
+	refs/remotes/*,commit)
+		# tracking branch
+		;;
+	refs/remotes/*,delete)
+		# delete tracking branch
+		if [ "$allowdeletebranch" != "true" ]; then
+			echo "*** Deleting a tracking branch is not allowed in this repository" >&2
+			exit 1
+		fi
+		;;
+	*)
+		# Anything else (is there anything else?)
+		echo "*** Update hook: unknown type of update to ref $refname of type $newrev_type" >&2
+		exit 1
+		;;
+esac
+
+# --- Finished
+exit 0

gym_minigrid/.orig_git/info/exclude

@@ -0,0 +1,6 @@
+# git ls-files --others --exclude-from=.git/info/exclude
+# Lines that start with '#' are comments.
+# For a project mostly in C, the following would be a good set of
+# exclude patterns (uncomment them if you want to use them):
+# *.[oa]
+# *~

gym_minigrid/.orig_git/logs/HEAD

@@ -0,0 +1 @@
+0000000000000000000000000000000000000000 69bf17f1d8fd026c6418d8a66a2448fbec679e4c Stefan Pranger <stefan.pranger@iaik.tugraz.at> 1672500021 +0100	clone: from gitea@git.pranger.xyz:sp/Minigrid.git

gym_minigrid/.orig_git/logs/refs/heads/ag_proof_of_concept

@@ -0,0 +1 @@
+0000000000000000000000000000000000000000 69bf17f1d8fd026c6418d8a66a2448fbec679e4c Stefan Pranger <stefan.pranger@iaik.tugraz.at> 1672500021 +0100	clone: from gitea@git.pranger.xyz:sp/Minigrid.git

gym_minigrid/.orig_git/logs/refs/remotes/origin/HEAD

@@ -0,0 +1 @@
+0000000000000000000000000000000000000000 9dd8245c7350404a2f2103a2d14dc2ef2a232d76 Stefan Pranger <stefan.pranger@iaik.tugraz.at> 1672500021 +0100	clone: from gitea@git.pranger.xyz:sp/Minigrid.git

gym_minigrid/.orig_git/packed-refs

@@ -0,0 +1,3 @@
+# pack-refs with: peeled fully-peeled sorted 
+69bf17f1d8fd026c6418d8a66a2448fbec679e4c refs/remotes/origin/ag_proof_of_concept
+9dd8245c7350404a2f2103a2d14dc2ef2a232d76 refs/remotes/origin/guided_rl

gym_minigrid/.orig_git/refs/heads/ag_proof_of_concept

@@ -0,0 +1 @@
+69bf17f1d8fd026c6418d8a66a2448fbec679e4c

gym_minigrid/.orig_git/refs/remotes/origin/HEAD

@@ -0,0 +1 @@
+ref: refs/remotes/origin/guided_rl

gym_minigrid/LICENSE

@@ -0,0 +1,202 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+      "Work" shall mean the work of authorship, whether in Source or
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+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
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+      PARTICULAR PURPOSE. You are solely responsible for determining the
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+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
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+
+   Copyright 2019 Maxime Chevalier-Boisvert
+   Copyright 2022 Farama Foundation
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
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+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

gym_minigrid/PKG-INFO

@@ -0,0 +1,111 @@
+Metadata-Version: 2.1
+Name: gym_minigrid
+Version: 1.2.2
+Summary: Minimalistic gridworld reinforcement learning environments
+Home-page: https://github.com/Farama-Foundation/gym-minigrid
+Author: Farama Foundation
+Author-email: jkterry@farama.org
+License: Apache
+Keywords: memory,environment,agent,rl,gym
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+Provides-Extra: testing
+License-File: LICENSE
+
+# MiniGrid (formerly gym-minigrid)
+
+[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://pre-commit.com/) 
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+There are other gridworld Gym environments out there, but this one is
+designed to be particularly simple, lightweight and fast. The code has very few
+dependencies, making it less likely to break or fail to install. It loads no
+external sprites/textures, and it can run at up to 5000 FPS on a Core i7
+laptop, which means you can run your experiments faster. A known-working RL
+implementation can be found [in this repository](https://github.com/lcswillems/torch-rl).
+
+Requirements:
+- Python 3.7 to 3.10
+- OpenAI Gym v0.26
+- NumPy 1.18+
+- Matplotlib (optional, only needed for display) - 3.0+
+
+Please use this bibtex if you want to cite this repository in your publications:
+
+```
+@misc{gym_minigrid,
+  author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman},
+  title = {Minimalistic Gridworld Environment for OpenAI Gym},
+  year = {2018},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/maximecb/gym-minigrid}},
+}
+```
+
+List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries):
+- [History Compression via Language Models in Reinforcement Learning.](https://proceedings.mlr.press/v162/paischer22a.html) (Johannes Kepler University Linz, PMLR 2022)
+- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://arxiv.org/abs/2202.02886) (Arizona State University, ICML 2022)
+- [How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://proceedings.mlr.press/v162/mavor-parker22a.html) (University College London, Boston University, ICML 2022)
+- [In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications](https://openreview.net/pdf?id=rUwm9wCjURV) (Imperial College London, ICLR 2022)
+- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://arxiv.org/abs/2111.13119) (Meta AI Research, NeurIPS 2021)
+- [Safe Policy Optimization with Local Generalized Linear Function Approximations](https://arxiv.org/abs/2111.04894) (IBM Research, Tsinghua University, NeurIPS 2021)
+- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://arxiv.org/abs/2106.02097) (Mila, McGill University, NeurIPS 2021)
+- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1118.pdf) (Tufts University, SIFT, AAMAS 2021)
+- [Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning](https://arxiv.org/abs/2102.04220) (UCL, AAMAS 2021)
+- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://openreview.net/forum?id=MtEE0CktZht) (Texas A&M University, Kuai Inc., ICLR 2021)
+- [Adversarially Guided Actor-Critic](https://openreview.net/forum?id=_mQp5cr_iNy) (INRIA, Google Brain, ICLR 2021)
+- [Information-theoretic Task Selection for Meta-Reinforcement Learning](https://papers.nips.cc/paper/2020/file/ec3183a7f107d1b8dbb90cb3c01ea7d5-Paper.pdf) (University of Leeds, NeurIPS 2020)
+- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/pdf/2012.08621.pdf) (UCB, December 2020)
+- [Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems](https://arxiv.org/abs/2010.08843) (McGill, October 2020)
+- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020)
+- [AllenAct: A Framework for Embodied AI Research](https://arxiv.org/pdf/2008.12760.pdf) (Allen Institute for AI, August 2020)
+- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, ICLR 2021)
+- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020)
+- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019)
+- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019)
+- [Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp)
+- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019)
+- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) 
+- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019)
+- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019)
+- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019)
+- [Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019)
+- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019)
+- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019)
+- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019)
+- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019)
+- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019)
+- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019)
+- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019)
+- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018)
+- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018)
+- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018)
+- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018)
+- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018)
+- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018)
+
+This environment has been built as part of work done at [Mila](https://mila.quebec). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles.
+
+## Installation
+
+There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically:
+
+```
+pip3 install gym-minigrid
+```
+
+Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`:
+
+```
+git clone https://github.com/maximecb/gym-minigrid.git
+cd gym-minigrid
+pip3 install -e .
+```
+

gym_minigrid/README.md

@@ -0,0 +1,525 @@
+# MiniGrid (formerly gym-minigrid)
+
+[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://pre-commit.com/) 
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+There are other gridworld Gym environments out there, but this one is
+designed to be particularly simple, lightweight and fast. The code has very few
+dependencies, making it less likely to break or fail to install. It loads no
+external sprites/textures, and it can run at up to 5000 FPS on a Core i7
+laptop, which means you can run your experiments faster. A known-working RL
+implementation can be found [in this repository](https://github.com/lcswillems/torch-rl).
+
+Requirements:
+- Python 3.7 to 3.10
+- OpenAI Gym v0.26
+- NumPy 1.18+
+- Matplotlib (optional, only needed for display) - 3.0+
+
+Please use this bibtex if you want to cite this repository in your publications:
+
+```
+@misc{gym_minigrid,
+  author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman},
+  title = {Minimalistic Gridworld Environment for OpenAI Gym},
+  year = {2018},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/maximecb/gym-minigrid}},
+}
+```
+
+List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries):
+- [History Compression via Language Models in Reinforcement Learning.](https://proceedings.mlr.press/v162/paischer22a.html) (Johannes Kepler University Linz, PMLR 2022)
+- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://arxiv.org/abs/2202.02886) (Arizona State University, ICML 2022)
+- [How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://proceedings.mlr.press/v162/mavor-parker22a.html) (University College London, Boston University, ICML 2022)
+- [In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications](https://openreview.net/pdf?id=rUwm9wCjURV) (Imperial College London, ICLR 2022)
+- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://arxiv.org/abs/2111.13119) (Meta AI Research, NeurIPS 2021)
+- [Safe Policy Optimization with Local Generalized Linear Function Approximations](https://arxiv.org/abs/2111.04894) (IBM Research, Tsinghua University, NeurIPS 2021)
+- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://arxiv.org/abs/2106.02097) (Mila, McGill University, NeurIPS 2021)
+- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1118.pdf) (Tufts University, SIFT, AAMAS 2021)
+- [Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning](https://arxiv.org/abs/2102.04220) (UCL, AAMAS 2021)
+- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://openreview.net/forum?id=MtEE0CktZht) (Texas A&M University, Kuai Inc., ICLR 2021)
+- [Adversarially Guided Actor-Critic](https://openreview.net/forum?id=_mQp5cr_iNy) (INRIA, Google Brain, ICLR 2021)
+- [Information-theoretic Task Selection for Meta-Reinforcement Learning](https://papers.nips.cc/paper/2020/file/ec3183a7f107d1b8dbb90cb3c01ea7d5-Paper.pdf) (University of Leeds, NeurIPS 2020)
+- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/pdf/2012.08621.pdf) (UCB, December 2020)
+- [Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems](https://arxiv.org/abs/2010.08843) (McGill, October 2020)
+- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020)
+- [AllenAct: A Framework for Embodied AI Research](https://arxiv.org/pdf/2008.12760.pdf) (Allen Institute for AI, August 2020)
+- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, ICLR 2021)
+- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020)
+- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019)
+- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019)
+- [Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp)
+- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019)
+- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) 
+- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019)
+- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019)
+- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019)
+- [Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019)
+- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019)
+- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019)
+- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019)
+- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019)
+- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019)
+- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019)
+- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019)
+- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018)
+- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018)
+- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018)
+- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018)
+- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018)
+- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018)
+
+This environment has been built as part of work done at [Mila](https://mila.quebec). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles.
+
+## Installation
+
+There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically:
+
+```
+pip3 install gym-minigrid
+```
+
+Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`:
+
+```
+git clone https://github.com/maximecb/gym-minigrid.git
+cd gym-minigrid
+pip3 install -e .
+```
+
+## Basic Usage
+
+There is a UI application which allows you to manually control the agent with the arrow keys:
+
+```
+./gym-minigrid/manual_control.py
+```
+
+The environment being run can be selected with the `--env` option, eg:
+
+```
+./gym-minigrid/manual_control.py --env MiniGrid-Empty-8x8-v0
+```
+
+## Reinforcement Learning
+
+If you want to train an agent with reinforcement learning, I recommend using the code found in the [torch-rl](https://github.com/lcswillems/torch-rl) repository. 
+This code has been tested and is known to work with this environment. The default hyper-parameters are also known to converge.
+
+A sample training command is:
+
+```
+cd torch-rl
+python3 -m scripts.train --env MiniGrid-Empty-8x8-v0 --algo ppo
+```
+
+## Wrappers
+
+MiniGrid is built to support tasks involving natural language and sparse rewards.
+The observations are dictionaries, with an 'image' field, partially observable
+view of the environment, a 'mission' field which is a textual string
+describing the objective the agent should reach to get a reward, and a 'direction'
+field which can be used as an optional compass. Using dictionaries makes it
+easy for you to add additional information to observations
+if you need to, without having to encode everything into a single tensor.
+
+There are a variety of wrappers to change the observation format available in [gym_minigrid/wrappers.py](/gym_minigrid/wrappers.py). 
+If your RL code expects one single tensor for observations, take a look at `FlatObsWrapper`. 
+There is also an `ImgObsWrapper` that gets rid of the 'mission' field in observations, leaving only the image field tensor.
+
+Please note that the default observation format is a partially observable view of the environment using a
+compact and efficient encoding, with 3 input values per visible grid cell, 7x7x3 values total.
+These values are **not pixels**. If you want to obtain an array of RGB pixels as observations instead,
+use the `RGBImgPartialObsWrapper`. You can use it as follows:
+
+```python
+import gym
+from gym_minigrid.wrappers import RGBImgPartialObsWrapper, ImgObsWrapper
+
+env = gym.make('MiniGrid-Empty-8x8-v0')
+env = RGBImgPartialObsWrapper(env) # Get pixel observations
+env = ImgObsWrapper(env) # Get rid of the 'mission' field
+obs, _ = env.reset() # This now produces an RGB tensor only
+```
+
+## Design
+
+Structure of the world:
+- The world is an NxM grid of tiles
+- Each tile in the grid world contains zero or one object
+  - Cells that do not contain an object have the value `None`
+- Each object has an associated discrete color (string)
+- Each object has an associated type (string)
+  - Provided object types are: wall, floor, lava, door, key, ball, box and goal
+- The agent can pick up and carry exactly one object (eg: ball or key)
+- To open a locked door, the agent has to be carrying a key matching the door's color
+
+Actions in the basic environment:
+- Turn left
+- Turn right
+- Move forward
+- Pick up an object
+- Drop the object being carried
+- Toggle (open doors, interact with objects)
+- Done (task completed, optional)
+
+Default tile/observation encoding:
+- Each tile is encoded as a 3 dimensional tuple: `(OBJECT_IDX, COLOR_IDX, STATE)` 
+- `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+- `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+By default, sparse rewards are given for reaching a green goal tile. A
+reward of 1 is given for success, and zero for failure. There is also an
+environment-specific time step limit for completing the task.
+You can define your own reward function by creating a class derived
+from `MiniGridEnv`. Extending the environment with new object types or new actions
+should be very easy. If you wish to do this, you should take a look at the
+[gym_minigrid/minigrid.py](gym_minigrid/minigrid.py) source file.
+
+## Included Environments
+
+The environments listed below are implemented in the [gym_minigrid/envs](/gym_minigrid/envs) directory.
+Each environment provides one or more configurations registered with OpenAI gym. Each environment
+is also programmatically tunable in terms of size/complexity, which is useful for curriculum learning
+or to fine-tune difficulty.
+
+### Empty environment
+
+This environment is an empty room, and the goal of the agent is to reach the
+green goal square, which provides a sparse reward. A small penalty is
+subtracted for the number of steps to reach the goal. This environment is
+useful, with small rooms, to validate that your RL algorithm works correctly,
+and with large rooms to experiment with sparse rewards and exploration.
+The random variants of the environment have the agent starting at a random
+position for each episode, while the regular variants have the agent always
+starting in the corner opposite to the goal.
+
+<p align="center">
+    <img src="figures/empty-env.png" width=250 alt="Figure of the empty environment">
+</p>
+
+Registered configurations: 
+- `MiniGrid-Empty-5x5-v0`
+- `MiniGrid-Empty-Random-5x5-v0`
+- `MiniGrid-Empty-6x6-v0`
+- `MiniGrid-Empty-Random-6x6-v0`
+- `MiniGrid-Empty-8x8-v0`
+- `MiniGrid-Empty-16x16-v0`
+
+### Four rooms environment
+
+Classic four room reinforcement learning environment. The agent must navigate
+in a maze composed of four rooms interconnected by 4 gaps in the walls. To
+obtain a reward, the agent must reach the green goal square. Both the agent
+and the goal square are randomly placed in any of the four rooms.
+
+<p align="center">
+    <img src="figures/four-rooms-env.png" width=380 alt="Figure of the four room environment">
+</p>
+
+Registered configurations: 
+- `MiniGrid-FourRooms-v0`
+
+### Door & key environment
+
+This environment has a key that the agent must pick up in order to unlock
+a goal and then get to the green goal square. This environment is difficult,
+because of the sparse reward, to solve using classical RL algorithms. It is
+useful to experiment with curiosity or curriculum learning.
+
+<p align="center">
+    <img src="figures/door-key-env.png" alt="Figure of the door key environment">
+</p>
+
+Registered configurations: 
+- `MiniGrid-DoorKey-5x5-v0`
+- `MiniGrid-DoorKey-6x6-v0`
+- `MiniGrid-DoorKey-8x8-v0`
+- `MiniGrid-DoorKey-16x16-v0`
+
+### Multi-room environment
+
+This environment has a series of connected rooms with doors that must be
+opened in order to get to the next room. The final room has the green goal
+square the agent must get to. This environment is extremely difficult to
+solve using RL alone. However, by gradually increasing the number of
+rooms and building a curriculum, the environment can be solved.
+
+<p align="center">
+    <img src="figures/multi-room.gif" width=416 height=424 alt="Figure of the Multi-room environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-MultiRoom-N2-S4-v0` (two small rooms)
+- `MiniGrid-MultiRoom-N4-S5-v0` (four rooms)
+- `MiniGrid-MultiRoom-N6-v0` (six rooms)
+
+### Fetch environment
+
+This environment has multiple objects of assorted types and colors. The
+agent receives a textual string as part of its observation telling it
+which object to pick up. Picking up the wrong object terminates the
+episode with zero reward.
+
+<p align="center">
+    <img src="figures/fetch-env.png" width=450 alt="Figure of the fetch environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-Fetch-5x5-N2-v0`
+- `MiniGrid-Fetch-6x6-N2-v0`
+- `MiniGrid-Fetch-8x8-N3-v0`
+
+### Go-to-door environment
+
+This environment is a room with four doors, one on each wall. The agent
+receives a textual (mission) string as input, telling it which door to go to,
+(eg: "go to the red door"). It receives a positive reward for performing the
+`done` action next to the correct door, as indicated in the mission string.
+
+<p align="center">
+    <img src="figures/gotodoor-6x6.png" width=400 alt="Figure of the go-to-door environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-GoToDoor-5x5-v0`
+- `MiniGrid-GoToDoor-6x6-v0`
+- `MiniGrid-GoToDoor-8x8-v0`
+
+### Put-near environment
+
+The agent is instructed through a textual string to pick up an object and
+place it next to another object. This environment is easy to solve with two
+objects, but difficult to solve with more, as it involves both textual
+understanding and spatial reasoning involving multiple objects.
+
+Registered configurations:
+- `MiniGrid-PutNear-6x6-N2-v0`
+- `MiniGrid-PutNear-8x8-N3-v0`
+
+### Red and blue doors environment
+
+The agent is randomly placed within a room with one red and one blue door
+facing opposite directions. The agent has to open the red door and then open
+the blue door, in that order. Note that, surprisingly, this environment is
+solvable without memory.
+
+Registered configurations:
+- `MiniGrid-RedBlueDoors-6x6-v0`
+- `MiniGrid-RedBlueDoors-8x8-v0`
+
+### Memory environment
+
+This environment is a memory test. The agent starts in a small room
+where it sees an object. It then has to go through a narrow hallway
+which ends in a split. At each end of the split there is an object,
+one of which is the same as the object in the starting room. The
+agent has to remember the initial object, and go to the matching
+object at split.
+
+Registered configurations:
+- `MiniGrid-MemoryS17Random-v0`
+- `MiniGrid-MemoryS13Random-v0`
+- `MiniGrid-MemoryS13-v0`
+- `MiniGrid-MemoryS11-v0`
+
+### Locked room environment
+
+The environment has six rooms, one of which is locked. The agent receives
+a textual mission string as input, telling it which room to go to in order
+to get the key that opens the locked room. It then has to go into the locked
+room in order to reach the final goal. This environment is extremely difficult
+to solve with vanilla reinforcement learning alone.
+
+Registered configurations:
+- `MiniGrid-LockedRoom-v0`
+
+### Key corridor environment
+
+This environment is similar to the locked room environment, but there are
+multiple registered environment configurations of increasing size,
+making it easier to use curriculum learning to train an agent to solve it.
+The agent has to pick up an object which is behind a locked door. The key is
+hidden in another room, and the agent has to explore the environment to find
+it. The mission string does not give the agent any clues as to where the
+key is placed. This environment can be solved without relying on language.
+
+<p align="center">
+    <img src="figures/KeyCorridorS3R1.png" width=250 alt="Figure of the Key Corridor for config S3R1">
+    <img src="figures/KeyCorridorS3R2.png" width=250 alt="Figure of the Key Corridor for config S3R2">
+    <img src="figures/KeyCorridorS3R3.png" width=250 alt="Figure of the Key Corridor for config S3R3">
+    <img src="figures/KeyCorridorS4R3.png" width=250 alt="Figure of the Key Corridor for config S4R3">
+    <img src="figures/KeyCorridorS5R3.png" width=250 alt="Figure of the Key Corridor for config S5R3">
+    <img src="figures/KeyCorridorS6R3.png" width=250 alt="Figure of the Key Corridor for config S6R3">
+</p>
+
+Registered configurations:
+- `MiniGrid-KeyCorridorS3R1-v0`
+- `MiniGrid-KeyCorridorS3R2-v0`
+- `MiniGrid-KeyCorridorS3R3-v0`
+- `MiniGrid-KeyCorridorS4R3-v0`
+- `MiniGrid-KeyCorridorS5R3-v0`
+- `MiniGrid-KeyCorridorS6R3-v0`
+
+### Unlock environment
+
+The agent has to open a locked door. This environment can be solved without
+relying on language.
+
+<p align="center">
+    <img src="figures/Unlock.png" width=200 alt="Figure of the unlock environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-Unlock-v0`
+
+### Unlock pickup environment
+
+The agent has to pick up a box which is placed in another room, behind a
+locked door. This environment can be solved without relying on language.
+
+<p align="center">
+    <img src="figures/UnlockPickup.png" width=250 alt="Figure of the unlock pickup environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-UnlockPickup-v0`
+
+### Blocked unlock pickup environment
+
+The agent has to pick up a box which is placed in another room, behind a
+locked door. The door is also blocked by a ball which the agent has to move
+before it can unlock the door. Hence, the agent has to learn to move the ball,
+pick up the key, open the door and pick up the object in the other room.
+This environment can be solved without relying on language.
+
+<p align="center">
+    <img src="figures/BlockedUnlockPickup.png" width=250 alt="Figure of the blocked-unlock-pickup environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-BlockedUnlockPickup-v0`
+
+## Obstructed maze environment
+
+The agent has to pick up a box which is placed in a corner of a 3x3 maze.
+The doors are locked, the keys are hidden in boxes and doors are obstructed
+by balls. This environment can be solved without relying on language.
+
+<p align="center">
+  <img src="figures/ObstructedMaze-1Dl.png" width="250">
+  <img src="figures/ObstructedMaze-1Dlh.png" width="250">
+  <img src="figures/ObstructedMaze-1Dlhb.png" width="250">
+  <img src="figures/ObstructedMaze-2Dl.png" width="100">
+  <img src="figures/ObstructedMaze-2Dlh.png" width="100">
+  <img src="figures/ObstructedMaze-2Dlhb.png" width="100">
+  <img src="figures/ObstructedMaze-1Q.png" width="250">
+  <img src="figures/ObstructedMaze-2Q.png" width="250">
+  <img src="figures/ObstructedMaze-4Q.png" width="250">
+</p>
+
+Registered configurations:
+- `MiniGrid-ObstructedMaze-1Dl-v0`
+- `MiniGrid-ObstructedMaze-1Dlh-v0`
+- `MiniGrid-ObstructedMaze-1Dlhb-v0`
+- `MiniGrid-ObstructedMaze-2Dl-v0`
+- `MiniGrid-ObstructedMaze-2Dlh-v0`
+- `MiniGrid-ObstructedMaze-2Dlhb-v0`
+- `MiniGrid-ObstructedMaze-1Q-v0`
+- `MiniGrid-ObstructedMaze-2Q-v0`
+- `MiniGrid-ObstructedMaze-Full-v0`
+
+## Distributional shift environment
+
+This environment is based on one of the DeepMind [AI safety gridworlds](https://github.com/deepmind/ai-safety-gridworlds).
+The agent starts in the top-left corner and must reach the goal which is in the top-right corner, but has to avoid stepping
+into lava on its way. The aim of this environment is to test an agent's ability to generalize. There are two slightly
+different variants of the environment, so that the agent can be trained on one variant and tested on the other.
+
+<p align="center">
+  <img src="figures/DistShift1.png" width=200 alt="Figure of the DistShift1 environment">
+  <img src="figures/DistShift2.png" width=200 alt="Figure of the DistShift2 environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-DistShift1-v0`
+- `MiniGrid-DistShift2-v0`
+
+## Lava gap environment
+
+The agent has to reach the green goal square at the opposite corner of the room,
+and must pass through a narrow gap in a vertical strip of deadly lava. Touching
+the lava terminate the episode with a zero reward. This environment is useful
+for studying safety and safe exploration.
+
+Registered configurations:
+- `MiniGrid-LavaGapS5-v0`
+- `MiniGrid-LavaGapS6-v0`
+- `MiniGrid-LavaGapS7-v0`
+
+<p align="center">
+  <img src="figures/LavaGapS6.png" width=200 alt="Figure of the LavaGap environment">
+</p>
+
+## Lava crossing environment
+
+The agent has to reach the green goal square on the other corner of the room
+while avoiding rivers of deadly lava which terminate the episode in failure.
+Each lava stream runs across the room either horizontally or vertically, and
+has a single crossing point which can be safely used;  Luckily, a path to the
+goal is guaranteed to exist. This environment is useful for studying safety and
+safe exploration.
+
+<p align="center">
+  <img src="figures/LavaCrossingS9N1.png" width=200 alt="Figure of the LavaCrossingS9N1 environment">
+  <img src="figures/LavaCrossingS9N2.png" width=200 alt="Figure of the LavaCrossingS9N2 environment">
+  <img src="figures/LavaCrossingS9N3.png" width=200 alt="Figure of the LavaCrossingS9N3 environment">
+  <img src="figures/LavaCrossingS11N5.png" width=250 alt="Figure of the LavaCrossingS11N5 environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-LavaCrossingS9N1-v0`
+- `MiniGrid-LavaCrossingS9N2-v0`
+- `MiniGrid-LavaCrossingS9N3-v0`
+- `MiniGrid-LavaCrossingS11N5-v0`
+
+## Simple crossing environment
+
+Similar to the `LavaCrossing` environment, the agent has to reach the green
+goal square on the other corner of the room, however lava is replaced by
+walls. This MDP is therefore much easier and maybe useful for quickly
+testing your algorithms.
+
+<p align="center">
+  <img src="figures/SimpleCrossingS9N1.png" width=200 alt="Figure of the SimpleCrossingS9N1 environment">
+  <img src="figures/SimpleCrossingS9N2.png" width=200 alt="Figure of the SimpleCrossingS9N2 environment">
+  <img src="figures/SimpleCrossingS9N3.png" width=200 alt="Figure of the SimpleCrossingS9N3 environment">
+  <img src="figures/SimpleCrossingS11N5.png" width=250 alt="Figure of the SimpleCrossingS11N5 environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-SimpleCrossingS9N1-v0`
+- `MiniGrid-SimpleCrossingS9N2-v0`
+- `MiniGrid-SimpleCrossingS9N3-v0`
+- `MiniGrid-SimpleCrossingS11N5-v0`
+
+### Dynamic obstacles environment
+
+This environment is an empty room with moving obstacles. 
+The goal of the agent is to reach the green goal square without colliding with any obstacle. 
+A large penalty is subtracted if the agent collides with an obstacle and the episode finishes. 
+This environment is useful to test Dynamic Obstacle Avoidance for mobile robots with Reinforcement Learning in Partial Observability.
+
+<p align="center">
+    <img src="figures/dynamic_obstacles.gif" alt="GIF of the Dynamic Obstacles environment">
+</p>
+
+Registered configurations:
+- `MiniGrid-Dynamic-Obstacles-5x5-v0`
+- `MiniGrid-Dynamic-Obstacles-Random-5x5-v0`
+- `MiniGrid-Dynamic-Obstacles-6x6-v0`
+- `MiniGrid-Dynamic-Obstacles-Random-6x6-v0`
+- `MiniGrid-Dynamic-Obstacles-8x8-v0`
+- `MiniGrid-Dynamic-Obstacles-16x16-v0`

gym_minigrid/gym_minigrid/Task.py

@@ -0,0 +1,230 @@
+from abc import ABC
+from typing import Iterable, List
+try:
+    from astar import find_path
+except:
+    print("Install with:")
+    print("pip install git+https://github.com/jrialland/python-astar.git")
+    raise Exception("Need to install astar")
+import numpy.random
+
+class Task(ABC):
+    # returns a bool, true if task is completed, false otherwise
+    def completed(self, pos, dir, carrying, env):
+        pass
+    # Returns the best action to solve this task
+    def get_best_action(self, pos, dir, carrying, env):
+        pass
+    # returns a string representing the task
+    def __repr__(self):
+        pass
+
+
+def get_plan(pos, dir, carrying, env, goal_pos):
+    def neighbors_fnct_a_star(node):
+        left = (node[0], node[1], node[3], -node[2])
+        right = (node[0], node[1], -node[3], node[2])
+        fwd_pos = node[0] + node[2], node[1] + node[3]
+        forward_cell = env.grid.get(*fwd_pos)
+        forward_background = env.grid.get_background(*fwd_pos)
+        my_color = "red" if env.agent_pos == pos else env.grid.get(*pos).color
+        background_color = forward_background.color if forward_background is not None else None
+
+        forward_pos_open = forward_cell is None or forward_cell.can_overlap()
+        forward_pos_not_agent = fwd_pos != env.agent_pos
+        background_is_my_color_or_none_or_i_am_red = (forward_background is None or
+                                                      (background_color == "lightblue" and my_color == "blue") or
+                                                      (background_color == "lightgreen" and my_color == "green") or
+                                                      (background_color == "lightgreen" and my_color == "purple") or # purple belongs to green region
+                                                      my_color == "red") # red can do whatever
+
+        if forward_pos_open and forward_pos_not_agent and  background_is_my_color_or_none_or_i_am_red:
+            forward = (node[0] + node[2], node[1] + node[3], node[2], node[3])
+            return forward, left, right
+        else:
+            return left, right
+
+    plan = find_path(
+        start=(pos[0], pos[1], dir[0], dir[1]),
+        goal=(goal_pos[0], goal_pos[1], dir[0], dir[1]),
+        neighbors_fnct=neighbors_fnct_a_star,
+        reversePath=False,
+        heuristic_cost_estimate_fnct=lambda a, b: abs(a[0] - b[0]) + abs(a[1] - b[1]),
+        distance_between_fnct=lambda a, b: 1.0,
+        is_goal_reached_fnct=lambda a, b: abs(a[0] - b[0]) + abs(a[1] - b[1]) <= 1
+    )
+    return list(plan) if plan is not None else None
+
+
+class GoTo(Task):
+    def __init__(self, goal_position):
+        self.goal_position = goal_position
+        self.plan = None
+
+    def completed(self, pos, dir, carrying, env):
+        return pos == self.goal_position
+
+    def get_best_action(self, pos, dir, carrying, env):
+
+            # if farther than 1 unit away, Run A*
+            if self.plan is None or len(self.plan) == 0:
+                self.plan = get_plan(pos, dir, carrying, env, self.goal_position)
+
+            # if we have a plan, but we are not in the state we should be, create new plan
+            if self.plan is not None:
+                current_state_maybe = self.plan.pop(0)
+                if current_state_maybe[0] != pos[0] or \
+                        current_state_maybe[1] != pos[1] or \
+                        current_state_maybe[2] != dir[0] or \
+                        current_state_maybe[3] != dir[1]:
+                    self.plan = None # get_plan(pos, dir, carrying, env, self.goal_position)
+                    return self.get_best_action(pos, dir, carrying, env)
+
+            if self.plan is None or len(self.plan) <= 1:  # this will only happen if the agent is blocked in somehow
+                return 6  # done, does nothing
+            next_state = self.plan[0]
+
+            # decide how to achieve next state
+            if abs(next_state[0] - pos[0]) == 1 or abs(next_state[1] - pos[1]) == 1:
+                return 2  # forward
+            elif next_state[2] == dir[1] and next_state[3] == -dir[0]:
+                return 0  # left
+            elif next_state[2] == -dir[1] and next_state[3] == dir[0]:
+                return 1  # right
+            else:  # something went wrong such as bumping into other agent, replan
+                self.plan = None
+                return self.get_best_action(pos, dir, carrying, env)
+
+    def __repr__(self):
+        return "Task: Go to position {}".format(self.goal_position)
+
+
+class PickUpObject(Task):
+    def __init__(self, obj_position, obj):
+        self.obj_position = obj_position
+        self.obj = obj
+        self.plan = None
+    def completed(self, pos, dir, carrying, env):
+        return carrying == self.obj
+    def get_best_action(self, pos, dir, carrying, env):
+        d_x = self.obj_position[0] - pos[0]
+        d_y = self.obj_position[1] - pos[1]
+        # if farther than 1 unit away, Run A*
+        if abs(d_x) + abs(d_y) > 1:
+            # if we have no plan, create new plan
+            if self.plan is None or len(self.plan) == 0:
+                self.plan = get_plan(pos, dir, carrying, env, self.obj_position)
+
+            # if we have a plan, but we are not in the state we should be, create new plan
+            if self.plan is not None:
+                current_state_maybe = self.plan.pop(0)
+                if current_state_maybe[0] != pos[0] or \
+                        current_state_maybe[1] != pos[1] or \
+                        current_state_maybe[2] != dir[0] or \
+                        current_state_maybe[3] != dir[1]:
+                    self.plan = get_plan(pos, dir, carrying, env, self.obj_position)
+
+            if self.plan is None:  # this will only happen if the agent is blocked in somehow
+                return 6  # done, does nothing
+            next_state = self.plan[0]
+
+
+            # decide how to achieve next state
+            if abs(next_state[0] - pos[0]) == 1 or abs(next_state[1] - pos[1]) == 1:
+                return 2 # forward
+            elif next_state[2] == dir[1] and next_state[3] == -dir[0]:
+                return 0 # left
+            elif next_state[2] == -dir[1] and next_state[3] == dir[0]:
+                return 1 # right
+            else: # something went wrong such as bumping into other agent, replan
+                self.plan = None
+                return self.get_best_action(pos, dir, carrying, env)
+
+        else:
+            if d_x != dir[0] or d_y != dir[1]:
+                return 0 # left
+            else:
+                return 3 # pickup
+
+    def __repr__(self):
+        return "Task: Pick up object at position {}".format(self.obj_position)
+
+
+class PlaceObject(Task):
+    def __init__(self, obj_position, obj):
+        self.obj_position = obj_position
+        self.obj = obj
+        self.plan = None
+
+    def completed(self, pos, dir, carrying, env):
+        return env.grid.get(*self.obj_position) == self.obj and carrying is None
+    def get_best_action(self, pos, dir, carrying, env):
+        d_x = self.obj_position[0] - pos[0]
+        d_y = self.obj_position[1] - pos[1]
+        # if farther than 1 unit away, go to it
+        if abs(d_x) + abs(d_y) > 1:
+            # if we have no plan, create new plan
+            if self.plan is None or len(self.plan) == 0:
+                self.plan = get_plan(pos, dir, carrying, env, self.obj_position)
+
+            # if we have a plan, but we are not in the state we should be, create new plan
+            if self.plan is not None:
+                current_state_maybe = self.plan.pop(0)
+                if current_state_maybe[0] != pos[0] or \
+                   current_state_maybe[1] != pos[1] or \
+                   current_state_maybe[2] != dir[0] or \
+                   current_state_maybe[3] != dir[1]:
+                    self.plan = get_plan(pos, dir, carrying, env, self.obj_position)
+
+            if self.plan is None: # this will only happen if the agent is blocked in somehow
+                return 6 # done, does nothing
+            next_state = self.plan[0]
+
+            # decide how to achieve next state
+            if abs(next_state[0] - pos[0]) == 1 or \
+               abs(next_state[1] - pos[1]) == 1:
+                return 2  # forward
+            elif next_state[2] == dir[1] and next_state[3] == -dir[0]:
+                return 0  # left
+            elif next_state[2] == -dir[1] and next_state[3] == dir[0]:
+                return 1  # right
+            else:  # something went wrong such as bumping into other agent, replan
+                self.plan = None
+                return self.get_best_action(pos, dir, carrying, env)
+        else:
+            if d_x != dir[0] or d_y != dir[1]:
+                return 0 # left
+            else:
+                return 4 # drop
+    def __repr__(self):
+        return "Task: Place object at position {}".format(self.obj_position)
+
+class DoNothing(Task):
+    def __init__(self):
+        pass
+    def completed(self, pos, dir, carrying, env):
+        return False
+    def get_best_action(self, pos, dir, carrying, env):
+        return 6 # done
+    def __repr__(self):
+        return "Task: Do nothing"
+class DoRandom(Task):
+    def __init__(self):
+        pass
+    def completed(self, pos, dir, carrying, env):
+        return False
+    def get_best_action(self, pos, dir, carrying, env):
+        return numpy.random.random_integers(0, 2, 1)# indexes between 0 and 2 are forward, left, right
+    def __repr__(self):
+        return "Task: Act randomly"
+class TaskManager:
+    def __init__(self, tasks:List[Task]):
+        self.tasks = tasks
+
+    def get_best_action(self, pos, dir, carrying, env):
+        if len(self.tasks) == 0:
+            raise Exception("Task empty")
+        if self.tasks[0].completed(pos, dir, carrying, env):
+            self.tasks.pop(0)
+        best_action = self.tasks[0].get_best_action(pos, dir, carrying, env)
+        return best_action

gym_minigrid/gym_minigrid/__init__.py

@@ -0,0 +1,625 @@
+from warnings import warn
+
+from gym.envs.registration import register
+
+from gym_minigrid import minigrid, roomgrid, wrappers
+
+from gym_minigrid.policyRepairEnv import *
+
+def register_minigrid_envs():
+
+    # gym_minigrid package deprecated in favor of minigrid
+    warn(
+        "The package name gym_minigrid has been deprecated in favor of minigrid. Please uninstall gym_minigrid and install minigrid with `pip install minigrid`. Future releases will be maintained under the new package name minigrid.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    # BlockedUnlockPickup
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-BlockedUnlockPickup-v0",
+        entry_point="gym_minigrid.envs:BlockedUnlockPickupEnv",
+    )
+
+    # LavaCrossing
+    # ----------------------------------------
+    register(
+        id="MiniGrid-LavaCrossingS9N1-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 9, "num_crossings": 1},
+    )
+
+    register(
+        id="MiniGrid-LavaCrossingS9N2-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 9, "num_crossings": 2},
+    )
+
+    register(
+        id="MiniGrid-LavaCrossingS9N3-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 9, "num_crossings": 3},
+    )
+
+    register(
+        id="MiniGrid-LavaCrossingS11N5-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 11, "num_crossings": 5},
+    )
+
+    # SimpleCrossing
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-SimpleCrossingS9N1-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 9, "num_crossings": 1, "obstacle_type": minigrid.Wall},
+    )
+
+    register(
+        id="MiniGrid-SimpleCrossingS9N2-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 9, "num_crossings": 2, "obstacle_type": minigrid.Wall},
+    )
+
+    register(
+        id="MiniGrid-SimpleCrossingS9N3-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 9, "num_crossings": 3, "obstacle_type": minigrid.Wall},
+    )
+
+    register(
+        id="MiniGrid-SimpleCrossingS11N5-v0",
+        entry_point="gym_minigrid.envs:CrossingEnv",
+        kwargs={"size": 11, "num_crossings": 5, "obstacle_type": minigrid.Wall},
+    )
+
+    # DistShift
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-DistShift1-v0",
+        entry_point="gym_minigrid.envs:DistShiftEnv",
+        kwargs={"strip2_row": 2},
+    )
+
+    register(
+        id="MiniGrid-DistShift2-v0",
+        entry_point="gym_minigrid.envs:DistShiftEnv",
+        kwargs={"strip2_row": 5},
+    )
+
+    # DoorKey
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-DoorKey-5x5-v0",
+        entry_point="gym_minigrid.envs:DoorKeyEnv",
+        kwargs={"size": 5},
+    )
+
+    register(
+        id="MiniGrid-DoorKey-6x6-v0",
+        entry_point="gym_minigrid.envs:DoorKeyEnv",
+        kwargs={"size": 5},
+    )
+
+    register(
+        id="MiniGrid-DoorKey-8x8-v0",
+        entry_point="gym_minigrid.envs:DoorKeyEnv",
+        kwargs={"size": 8},
+    )
+
+    register(
+        id="MiniGrid-DoorKey-16x16-v0",
+        entry_point="gym_minigrid.envs:DoorKeyEnv",
+        kwargs={"size": 16},
+    )
+
+    # Dynamic-Obstacles
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-Dynamic-Obstacles-5x5-v0",
+        entry_point="gym_minigrid.envs:DynamicObstaclesEnv",
+        kwargs={"size": 5, "n_obstacles": 2},
+    )
+
+    register(
+        id="MiniGrid-Dynamic-Obstacles-Random-5x5-v0",
+        entry_point="gym_minigrid.envs:DynamicObstaclesEnv",
+        kwargs={"size": 5, "agent_start_pos": None, "n_obstacles": 2},
+    )
+
+    register(
+        id="MiniGrid-Dynamic-Obstacles-6x6-v0",
+        entry_point="gym_minigrid.envs:DynamicObstaclesEnv",
+        kwargs={"size": 6, "n_obstacles": 3},
+    )
+
+    register(
+        id="MiniGrid-Dynamic-Obstacles-Random-6x6-v0",
+        entry_point="gym_minigrid.envs:DynamicObstaclesEnv",
+        kwargs={"size": 6, "agent_start_pos": None, "n_obstacles": 3},
+    )
+
+    register(
+        id="MiniGrid-Dynamic-Obstacles-8x8-v0",
+        entry_point="gym_minigrid.envs:DynamicObstaclesEnv",
+    )
+
+    register(
+        id="MiniGrid-Dynamic-Obstacles-16x16-v0",
+        entry_point="gym_minigrid.envs:DynamicObstaclesEnv",
+        kwargs={"size": 16, "n_obstacles": 8},
+    )
+
+    # Empty
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-Empty-5x5-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+        kwargs={"size": 5},
+    )
+
+    register(
+        id="MiniGrid-Empty-Random-5x5-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+        kwargs={"size": 5, "agent_start_pos": None},
+    )
+
+    register(
+        id="MiniGrid-Empty-6x6-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+        kwargs={"size": 6},
+    )
+
+    register(
+        id="MiniGrid-Empty-Random-6x6-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+        kwargs={"size": 6, "agent_start_pos": None},
+    )
+
+    register(
+        id="MiniGrid-Empty-8x8-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+    )
+
+    register(
+        id="MiniGrid-Empty-11x11-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+        kwargs={"size": 11},
+    )
+
+    register(
+        id="MiniGrid-Empty-16x16-v0",
+        entry_point="gym_minigrid.envs:EmptyEnv",
+        kwargs={"size": 16},
+    )
+
+    # Fetch
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-Fetch-5x5-N2-v0",
+        entry_point="gym_minigrid.envs:FetchEnv",
+        kwargs={"size": 5, "numObjs": 2},
+    )
+
+    register(
+        id="MiniGrid-Fetch-6x6-N2-v0",
+        entry_point="gym_minigrid.envs:FetchEnv",
+        kwargs={"size": 6, "numObjs": 2},
+    )
+
+    register(id="MiniGrid-Fetch-8x8-N3-v0", entry_point="gym_minigrid.envs:FetchEnv")
+
+    # FourRooms
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-FourRooms-v0",
+        entry_point="gym_minigrid.envs:FourRoomsEnv",
+    )
+
+    # GoToDoor
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-GoToDoor-5x5-v0",
+        entry_point="gym_minigrid.envs:GoToDoorEnv",
+    )
+
+    register(
+        id="MiniGrid-GoToDoor-6x6-v0",
+        entry_point="gym_minigrid.envs:GoToDoorEnv",
+        kwargs={"size": 6},
+    )
+
+    register(
+        id="MiniGrid-GoToDoor-8x8-v0",
+        entry_point="gym_minigrid.envs:GoToDoorEnv",
+        kwargs={"size": 8},
+    )
+
+    # GoToObject
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-GoToObject-6x6-N2-v0",
+        entry_point="gym_minigrid.envs:GoToObjectEnv",
+    )
+
+    register(
+        id="MiniGrid-GoToObject-8x8-N2-v0",
+        entry_point="gym_minigrid.envs:GoToObjectEnv",
+        kwargs={"size": 8, "numObjs": 2},
+    )
+
+    # KeyCorridor
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-KeyCorridorS3R1-v0",
+        entry_point="gym_minigrid.envs:KeyCorridorEnv",
+        kwargs={"room_size": 3, "num_rows": 1},
+    )
+
+    register(
+        id="MiniGrid-KeyCorridorS3R2-v0",
+        entry_point="gym_minigrid.envs:KeyCorridorEnv",
+        kwargs={"room_size": 3, "num_rows": 2},
+    )
+
+    register(
+        id="MiniGrid-KeyCorridorS3R3-v0",
+        entry_point="gym_minigrid.envs:KeyCorridorEnv",
+        kwargs={"room_size": 3, "num_rows": 3},
+    )
+
+    register(
+        id="MiniGrid-KeyCorridorS4R3-v0",
+        entry_point="gym_minigrid.envs:KeyCorridorEnv",
+        kwargs={"room_size": 4, "num_rows": 3},
+    )
+
+    register(
+        id="MiniGrid-KeyCorridorS5R3-v0",
+        entry_point="gym_minigrid.envs:KeyCorridorEnv",
+        kwargs={"room_size": 5, "num_rows": 3},
+    )
+
+    register(
+        id="MiniGrid-KeyCorridorS6R3-v0",
+        entry_point="gym_minigrid.envs:KeyCorridorEnv",
+        kwargs={"room_size": 6, "num_rows": 3},
+    )
+
+    # LavaGap
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-LavaGapS5-v0",
+        entry_point="gym_minigrid.envs:LavaGapEnv",
+        kwargs={"size": 5},
+    )
+
+    register(
+        id="MiniGrid-LavaGapS6-v0",
+        entry_point="gym_minigrid.envs:LavaGapEnv",
+        kwargs={"size": 6},
+    )
+
+    register(
+        id="MiniGrid-LavaGapS7-v0",
+        entry_point="gym_minigrid.envs:LavaGapEnv",
+        kwargs={"size": 7},
+    )
+
+    # LockedRoom
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-LockedRoom-v0",
+        entry_point="gym_minigrid.envs:LockedRoomEnv",
+    )
+
+    # Memory
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-MemoryS17Random-v0",
+        entry_point="gym_minigrid.envs:MemoryEnv",
+        kwargs={"size": 17, "random_length": True},
+    )
+
+    register(
+        id="MiniGrid-MemoryS13Random-v0",
+        entry_point="gym_minigrid.envs:MemoryEnv",
+        kwargs={"size": 13, "random_length": True},
+    )
+
+    register(
+        id="MiniGrid-MemoryS13-v0",
+        entry_point="gym_minigrid.envs:MemoryEnv",
+        kwargs={"size": 13},
+    )
+
+    register(
+        id="MiniGrid-MemoryS11-v0",
+        entry_point="gym_minigrid.envs:MemoryEnv",
+        kwargs={"size": 11},
+    )
+
+    register(
+        id="MiniGrid-MemoryS9-v0",
+        entry_point="gym_minigrid.envs:MemoryEnv",
+        kwargs={"size": 9},
+    )
+
+    register(
+        id="MiniGrid-MemoryS7-v0",
+        entry_point="gym_minigrid.envs:MemoryEnv",
+        kwargs={"size": 7},
+    )
+
+    # MultiRoom
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-MultiRoom-N2-S4-v0",
+        entry_point="gym_minigrid.envs:MultiRoomEnv",
+        kwargs={"minNumRooms": 2, "maxNumRooms": 2, "maxRoomSize": 4},
+    )
+
+    register(
+        id="MiniGrid-MultiRoom-N4-S5-v0",
+        entry_point="gym_minigrid.envs:MultiRoomEnv",
+        kwargs={"minNumRooms": 6, "maxNumRooms": 6, "maxRoomSize": 5},
+    )
+
+    register(
+        id="MiniGrid-MultiRoom-N6-v0",
+        entry_point="gym_minigrid.envs:MultiRoomEnv",
+        kwargs={"minNumRooms": 6, "maxNumRooms": 6},
+    )
+
+    # ObstructedMaze
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-ObstructedMaze-1Dl-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_1Dlhb",
+        kwargs={"key_in_box": False, "blocked": False},
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-1Dlh-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_1Dlhb",
+        kwargs={"key_in_box": True, "blocked": False},
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-1Dlhb-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_1Dlhb",
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-2Dl-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_Full",
+        kwargs={
+            "agent_room": (2, 1),
+            "key_in_box": False,
+            "blocked": False,
+            "num_quarters": 1,
+            "num_rooms_visited": 4,
+        },
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-2Dlh-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_Full",
+        kwargs={
+            "agent_room": (2, 1),
+            "key_in_box": True,
+            "blocked": False,
+            "num_quarters": 1,
+            "num_rooms_visited": 4,
+        },
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-2Dlhb-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_Full",
+        kwargs={
+            "agent_room": (2, 1),
+            "key_in_box": True,
+            "blocked": True,
+            "num_quarters": 1,
+            "num_rooms_visited": 4,
+        },
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-1Q-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_Full",
+        kwargs={
+            "agent_room": (1, 1),
+            "key_in_box": True,
+            "blocked": True,
+            "num_quarters": 1,
+            "num_rooms_visited": 5,
+        },
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-2Q-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_Full",
+        kwargs={
+            "agent_room": (2, 1),
+            "key_in_box": True,
+            "blocked": True,
+            "num_quarters": 2,
+            "num_rooms_visited": 11,
+        },
+    )
+
+    register(
+        id="MiniGrid-ObstructedMaze-Full-v0",
+        entry_point="gym_minigrid.envs:ObstructedMaze_Full",
+    )
+
+    # Playground
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-Playground-v0",
+        entry_point="gym_minigrid.envs:PlaygroundEnv",
+    )
+
+    # PutNear
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-PutNear-6x6-N2-v0",
+        entry_point="gym_minigrid.envs:PutNearEnv",
+    )
+
+    register(
+        id="MiniGrid-PutNear-8x8-N3-v0",
+        entry_point="gym_minigrid.envs:PutNearEnv",
+        kwargs={"size": 8, "numObjs": 3},
+    )
+
+    # RedBlueDoors
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-RedBlueDoors-6x6-v0",
+        entry_point="gym_minigrid.envs:RedBlueDoorEnv",
+        kwargs={"size": 6},
+    )
+
+    register(
+        id="MiniGrid-RedBlueDoors-8x8-v0",
+        entry_point="gym_minigrid.envs:RedBlueDoorEnv",
+    )
+
+    # Unlock
+    # ----------------------------------------
+
+    register(id="MiniGrid-Unlock-v0", entry_point="gym_minigrid.envs:UnlockEnv")
+
+    # UnlockPickup
+    # ----------------------------------------
+
+    register(
+        id="MiniGrid-UnlockPickup-v0",
+        entry_point="gym_minigrid.envs:UnlockPickupEnv",
+    )
+
+
+
+    # GuidedRLExperiments
+    # ----------------------------------------
+
+    #register(
+    #    id="MiniGrid-GuidedRLExperiments-TwoRooms-v0",
+    #    entry_point="gym_minigrid.envs:TwoRooms",
+    #)
+
+    ## AgExperiments
+    ## ----------------------------------------
+
+    #register(
+    #    id="MiniGrid-AgExperiments-DeliveryStations-v0",
+    #    entry_point="gym_minigrid.envs:DeliveryStations",
+    #)
+
+    #register(
+    #    id="MiniGrid-AgExperiments-DeliveryStationsWithRows-v0",
+    #    entry_point="gym_minigrid.envs:DeliveryStationsWithRows",
+    #)
+
+    #register(
+    #    id="MiniGrid-WareHouse",
+    #    entry_point="gym_minigrid.envs:WareHouse",
+    #)
+    #register(
+    #    id="MiniGrid-AgExperiments-DeliveryStationsSmall-v0",
+    #    entry_point="gym_minigrid.envs:DeliveryStationsSmall",
+    #)
+    #register(
+    #    id="MiniGrid-AgExperiments-DeliveryStationsSmallNoAg-v0",
+    #    entry_point="gym_minigrid.envs:DeliveryStationsSmallNoAg",
+    #)
+
+    register(
+        id="Testing-v0",
+        entry_point="gym_minigrid.envs:Testing",
+    )
+
+    register(
+        id="LavaObstaclesMini-v0",
+        entry_point="gym_minigrid.envs:LavaObstaclesMini",
+    )
+
+    register(
+        id="LavaSymmetricMini-v0",
+        entry_point="gym_minigrid.envs:LavaSymmetricMini",
+    )
+
+    register(
+        id="SlipperyMini-v0",
+        entry_point="gym_minigrid.envs:SlipperyMini",
+    )
+
+    # Testing Playground
+
+    register(
+        id="Abagarion",
+        entry_point="gym_minigrid.envs:Abagarion",
+    )
+
+    register(
+        id="ColumnsMini-v0",
+        entry_point="gym_minigrid.envs:ColumnsMini",
+    )
+
+    register(
+        id="ExperimentSlippery-v0",
+        entry_point="gym_minigrid.envs:ExperimentSlippery",
+    )
+
+    #cliff walking
+
+    register(
+        id="MyCliffWalking-v0",
+        entry_point="gym_minigrid.envs:CliffWalking",
+    )
+
+    register(
+        id="MyCliffWalking-S11-v0",
+        entry_point="gym_minigrid.envs:CliffWalking",
+        kwargs={"size": 11, "nr_cliffs":2},
+    )
+
+    register(
+        id="MyCliffWalking-S16-v0",
+        entry_point="gym_minigrid.envs:CliffWalking",
+        kwargs={"size": 16, "nr_cliffs":3},
+    )
+
+    register(
+        id="MyCliffWalking-S20-v0",
+        entry_point="gym_minigrid.envs:CliffWalking",
+        kwargs={"size": 20, "nr_cliffs":4},
+    )
+    register(
+        id="MyCliffWalking-S25-v0",
+        entry_point="gym_minigrid.envs:CliffWalking",
+        kwargs={"size": 25, "nr_cliffs":5},
+    )

gym_minigrid/gym_minigrid/benchmark.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+import time
+
+import gym
+
+from gym_minigrid.wrappers import ImgObsWrapper, RGBImgPartialObsWrapper
+
+
+def benchmark(env_id, num_resets, num_frames):
+    env = gym.make(env_id, render_mode="rgb_array")
+    # Benchmark env.reset
+    t0 = time.time()
+    for i in range(num_resets):
+        env.reset()
+    t1 = time.time()
+    dt = t1 - t0
+    reset_time = (1000 * dt) / num_resets
+
+    # Benchmark rendering
+    t0 = time.time()
+    for i in range(num_frames):
+        env.render()
+    t1 = time.time()
+    dt = t1 - t0
+    frames_per_sec = num_frames / dt
+
+    # Create an environment with an RGB agent observation
+    env = gym.make(env_id, render_mode="rgb_array")
+    env = RGBImgPartialObsWrapper(env)
+    env = ImgObsWrapper(env)
+
+    env.reset()
+    # Benchmark rendering
+    t0 = time.time()
+    for i in range(num_frames):
+        obs, reward, terminated, truncated, info = env.step(0)
+    t1 = time.time()
+    dt = t1 - t0
+    agent_view_fps = num_frames / dt
+
+    print(f"Env reset time: {reset_time:.1f} ms")
+    print(f"Rendering FPS : {frames_per_sec:.0f}")
+    print(f"Agent view FPS: {agent_view_fps:.0f}")
+
+    env.close()
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--env-id",
+        dest="env_id",
+        help="gym environment to load",
+        default="MiniGrid-LavaGapS7-v0",
+    )
+    parser.add_argument("--num_resets", default=200)
+    parser.add_argument("--num_frames", default=5000)
+    args = parser.parse_args()
+    benchmark(args.env_id, args.num_resets, args.num_frames)

gym_minigrid/gym_minigrid/envs/__init__.py

@@ -0,0 +1,40 @@
+from gym_minigrid.envs.blockedunlockpickup import BlockedUnlockPickupEnv
+from gym_minigrid.envs.crossing import CrossingEnv
+from gym_minigrid.envs.distshift import DistShiftEnv
+from gym_minigrid.envs.doorkey import DoorKeyEnv
+from gym_minigrid.envs.dynamicobstacles import DynamicObstaclesEnv
+from gym_minigrid.envs.empty import EmptyEnv
+from gym_minigrid.envs.fetch import FetchEnv
+from gym_minigrid.envs.fourrooms import FourRoomsEnv
+from gym_minigrid.envs.gotodoor import GoToDoorEnv
+from gym_minigrid.envs.gotoobject import GoToObjectEnv
+from gym_minigrid.envs.keycorridor import KeyCorridorEnv
+from gym_minigrid.envs.lavagap import LavaGapEnv
+from gym_minigrid.envs.lockedroom import LockedRoom, LockedRoomEnv
+from gym_minigrid.envs.memory import MemoryEnv
+from gym_minigrid.envs.multiroom import MultiRoom, MultiRoomEnv
+from gym_minigrid.envs.obstructedmaze import (
+    ObstructedMaze_1Dlhb,
+    ObstructedMaze_Full,
+    ObstructedMazeEnv,
+)
+from gym_minigrid.envs.playground import PlaygroundEnv
+from gym_minigrid.envs.putnear import PutNearEnv
+from gym_minigrid.envs.redbluedoors import RedBlueDoorEnv
+from gym_minigrid.envs.unlock import UnlockEnv
+from gym_minigrid.envs.unlockpickup import UnlockPickupEnv
+#from gym_minigrid.envs.guidedRLExperiments import TwoRooms
+#from gym_minigrid.envs.agExperiments import DeliveryStations
+#from gym_minigrid.envs.agSmall import  DeliveryStationsSmall
+#from gym_minigrid.envs.small import  DeliveryStationsSmallNoAg
+#from gym_minigrid.envs.warehouse import WareHouse
+from gym_minigrid.envs.testing import Testing
+import gym_minigrid.policyRepairEnv
+from gym_minigrid.envs.lavaObstaclesMini import LavaObstaclesMini
+from gym_minigrid.envs.lavaSymmetricMini import LavaSymmetricMini
+from gym_minigrid.envs.slipperyMini import SlipperyMini
+from gym_minigrid.envs.abagarion import Abagarion
+from gym_minigrid.envs.columnsMini import ColumnsMini
+from gym_minigrid.envs.slipperyMini import SlipperyMini
+from gym_minigrid.envs.experimentSlippery import ExperimentSlippery
+from gym_minigrid.envs.cliffWalking import CliffWalking

gym_minigrid/gym_minigrid/envs/abagarion.py

@@ -0,0 +1,67 @@
+# boilder plate code stolen from slipperyMini.py
+
+# 3x3 Lava + Slippery Environment: 
+# WGWGWGWGWG
+# WGXR    WG
+# WGSBVR  WG
+# WGSBSBGGWG
+# WGWGWGWGWG
+
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, SlipperyNorth, Wall, HeatMapTile, Lava
+from gym_minigrid.policyRepairEnv import PolicyRepairEnv
+from gym_minigrid.minigrid import run_BFS_reward
+
+class Abagarion(PolicyRepairEnv):
+
+    def __init__(self, size=5, agent_start_pos=(1, 1), agent_start_dir=0, **kwargs):
+        super().__init__(
+            size=size,
+            agent_start_pos=agent_start_pos,
+            agent_start_dir=agent_start_dir,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), width - 2, height - 2)
+        
+        self.put_obj(Lava(), 2, 2)
+        self.put_obj(SlipperyNorth(), 1, 2)
+        self.put_obj(SlipperyNorth(), 1, 3)
+        self.put_obj(SlipperyNorth(), 2, 3)
+
+        # Place the agent
+        if not self.agent_pos or (self.agent_pos[0] == -1 and self.agent_pos[1] == -1 and self.agent_dir == -1):
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+
+        self.bfs_reward = run_BFS_reward(self.grid, (width - 2, height - 2))
+        self.mission = "get to the green goal square"
+
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        #bfs reward
+        reward = self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward -=100
+
+        if self.render_mode == "human":
+            print("step: {}, reward: {}, info: {}\t\t\tthat terminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/agExperimentSetTwo.py

@@ -0,0 +1,172 @@
+import numpy as np
+from gym import spaces
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, Floor, Adversary, Box, SlipperyNorth
+from gym_minigrid.Task import DoRandom, TaskManager, DoNothing, GoTo, PlaceObject, PickUpObject, Task
+
+deliveryStation1Tile = Floor("blue")
+deliveryStation2Tile = Floor("green")
+deliveryStationRLTile = Floor("red")
+deliveryRegion1Tile = Floor("lightblue")
+deliveryRegion2Tile = Floor("lightgreen")
+
+def random_boxes_in_region(env, amount, xTop, yTop, width, height, agent_pos, color="red"):
+    totalBoxCount = amount
+    while totalBoxCount > 0:
+        x = np.random.choice(range(xTop, width - 1))
+        y = np.random.choice(range(yTop, height - 1))
+        if env.grid.get(x,y) == None and (x,y) != agent_pos:
+            env.put_obj(Box(color),x,y)
+            totalBoxCount -= 1
+
+class WareHouse(MiniGridEnv):
+
+    def __init__(self, agent_start_pos=(1, 7), agent_start_dir=0, sb3_mode=False, width=7, **kwargs):
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "Avoid crashing into the other agents."
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            width=width,
+            height=17,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        assert(width > 6)
+        self.grid = Grid(width, height)
+
+        self.grid.set_background(1,7,deliveryRegion1Tile)
+        self.grid.set_background(1,8,deliveryRegion1Tile)
+        self.grid.set_background(1,9,deliveryRegion1Tile)
+
+        self.adversaries = []
+        self.add_adversary(5,5,"blue",1)
+        self.add_adversary(10,9,"green",3)
+
+        self.grid.wall_rect(0, 0, width, height)
+        self.grid.vert_wall(1,1 , 6)
+        self.grid.vert_wall(1,10, 6)
+        
+        cols = (self.width - 4) // 3
+        for r in range(0, cols):
+            print(r)
+            self.grid.vert_wall(4 + r * 3,2 , 5)
+            self.grid.vert_wall(4 + r * 3,10, 5)
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+        random_boxes_in_region(self, 1, 3,1, self.width-1, self.height-1, self.agent_pos, "red")
+        random_boxes_in_region(self, 1, 3,1, self.width-1, self.height-1, self.agent_pos, "blue")
+        random_boxes_in_region(self, 1, 3,1, self.width-1, self.height-1, self.agent_pos, "green")
+
+        self.mission = "Avoid crashing into the other agents."
+
+    def step(self, action):
+        self.reward = 0.0
+
+
+        # need to augment this just like the agent, and we can hardcode a policy to follow
+        for adversary in self.adversaries:
+            blocked_positions = [adv.cur_pos for adv in self.adversaries]
+            agent_pos = self.agent_pos
+            advsersary_action = self.get_adversary_action(adversary)
+            self.move_adversary(adversary, advsersary_action, blocked_positions, agent_pos)
+
+        # get obs from env
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        # reward function and done function are custom
+        reward = self.reward
+        terminated = self.done
+
+        return obs, reward, terminated, truncated, info
+
+    # Todo make this an intelligent adversary policy
+    def get_adversary_action(self, adversary):
+        return adversary.task_manager.get_best_action(adversary.cur_pos, adversary.dir_vec(), adversary.carrying, self)
+
+
+    # Moves the adversary according to current policy, code copy pasted from minigrid.step
+    def move_adversary(self, adversary, action, blocked_positions, agent_pos):
+        # fetch current location and forward location
+        cur_pos = adversary.cur_pos
+        current_cell = self.grid.get(*adversary.cur_pos)
+        fwd_pos = cur_pos + adversary.dir_vec()
+        fwd_cell = self.grid.get(*fwd_pos)
+
+
+        if action == self.actions.left:
+            adversary.adversary_dir -= 1
+            if adversary.adversary_dir < 0:
+                adversary.adversary_dir += 4
+
+        # Rotate right
+        elif action == self.actions.right:
+            adversary.adversary_dir = (adversary.adversary_dir + 1) % 4
+
+        # Move forward
+        elif action == self.actions.forward and isinstance(current_cell, SlipperyNorth):
+            print("TODO: actual slippery obs, for now: Teleporting the agent")
+            # TODO also what if blocked, see below
+            adversary.cur_pos = (3, 3)
+
+        elif action == self.actions.forward and not isinstance(current_cell, SlipperyNorth):
+            if tuple(fwd_pos) == agent_pos:
+                self.reward -= 0.1
+
+            elif (fwd_cell is None or fwd_cell.can_overlap()) and not tuple(fwd_pos) in blocked_positions:
+                adversary.cur_pos = tuple(fwd_pos)
+
+        # Pick up an object
+        elif action == self.actions.pickup:
+            if fwd_cell and fwd_cell.can_pickup():
+                if adversary.carrying is None:
+                    adversary.carrying = fwd_cell
+                    adversary.carrying.cur_pos = np.array([-1, -1])
+                    self.grid.set(fwd_pos[0], fwd_pos[1], None)
+
+        # Drop an object
+        elif action == self.actions.drop:
+            if not fwd_cell and adversary.carrying:
+                self.grid.set(fwd_pos[0], fwd_pos[1], adversary.carrying)
+                adversary.carrying.cur_pos = fwd_pos
+                adversary.carrying = None
+
+        # Toggle/activate an object
+        elif action == self.actions.toggle:
+            if fwd_cell:
+                fwd_cell.toggle(self, fwd_pos)
+
+        # Done action (not used by default)
+        elif action == self.actions.done:
+            pass
+
+        else:
+            raise ValueError(f"Unknown action: {action}")
+
+        # finally update the env with these changes
+        self.grid.set(*cur_pos, None)
+        self.grid.set(*adversary.cur_pos, adversary)
+
+    def get_adversaries(self):
+        return [adversary.color for adversary in self.adversaries]
+
+    def get_property_map(self):
+        return { "blue" : ("SafetyAGBlue", 'concat(<_, PreSafety, lambda=0.89> <<Blue>> Pmax=? [ G !"BlueOnGreen"  ], <SafetyAGBlue, PreSafety, lambda=0.89> <<Agent>> Pmax=? [G<10 !"crash" ] );'), "green": ("SafetyAGGreen", 'concat(<_, PreSafety, lambda=0.89> <<Green>> Pmax=? [ G !"GreenOnBlue"  ], <SafetyAGGreen, PreSafety, lambda=0.89> <<Agent>> Pmax=? [G<10 !"crash" ] );') }
+
+    def get_shield_info(self):
+        #adv_positions = [(adv.color, adv.cur_pos[0], adv.cur_pos[1]) for adv in self.adversaries]
+        adv_positions = {adv.color : adv.cur_pos for adv in self.adversaries}
+        adv_directions = [adv.dir_vec() for adv in self.adversaries]
+        return self.agent_pos, self.agent_dir, adv_positions, adv_directions

gym_minigrid/gym_minigrid/envs/agExperiments.py

@@ -0,0 +1,498 @@
+import numpy as np
+from gym import spaces
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, Floor, Adversary, Box, SlipperyNorth
+from gym_minigrid.Task import DoRandom, TaskManager, DoNothing, GoTo, PlaceObject, PickUpObject, Task
+
+deliveryStation1Tile = Floor("blue")
+deliveryStation2Tile = Floor("green")
+deliveryStationRLTile = Floor("red")
+deliveryRegion1Tile = Floor("lightblue")
+deliveryRegion2Tile = Floor("lightgreen")
+
+class DeliveryStations(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment is an empty room, and the goal of the agent is to reach the
+    green goal square, which provides a sparse reward. A small penalty is
+    subtracted for the number of steps to reach the goal. This environment is
+    useful, with small rooms, to validate that your RL algorithm works
+    correctly, and with large rooms to experiment with sparse rewards and
+    exploration. The random variants of the environment have the agent starting
+    at a random position for each episode, while the regular variants have the
+    agent always starting in the corner opposite to the goal.
+
+    ### Mission Space
+
+    "get to the green goal square"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-Empty-5x5-v0`
+    - `MiniGrid-Empty-Random-5x5-v0`
+    - `MiniGrid-Empty-6x6-v0`
+    - `MiniGrid-Empty-Random-6x6-v0`
+    - `MiniGrid-Empty-8x8-v0`
+    - `MiniGrid-Empty-16x16-v0`
+
+    """
+
+    def __init__(self, agent_start_pos=(1, 1), agent_start_dir=0, highlight:bool=False,
+                 reward_type:str='Sparse', number_adversaries:int=2, max_steps=200,
+                 shield_gamma=1.0, shield_horizon=30, collision_penalty=0.1, **kwargs):
+        assert reward_type == 'Sparse' or reward_type == 'Dense', "Must select either 'Sparse' or 'Dense' reward"
+        assert 0 <= number_adversaries <= 2, "Must select either 0, 1, or 2 adversaries"
+
+        self.collision_penalty = collision_penalty
+        self.shield_gamma = shield_gamma
+        self.shield_horizon = shield_horizon
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+        self.number_adversaries = number_adversaries
+        self.reward_type = reward_type
+
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "Avoid crashing into the other agents."
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            width=13,
+            height=13,
+            # Set this to True for maximum speed
+            highlight=highlight,
+            see_through_walls=True,
+            max_steps=max_steps,
+            **kwargs
+        )
+
+
+    def _gen_grid(self, width, height):
+        self.use_green_adversary = self.number_adversaries == 2
+        self.use_blue_adversary = self.number_adversaries >= 1
+
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # add walls
+        self.grid.wall_rect(0, 0, width, height) # outside border
+        self.grid.horz_wall(2, 2, width - 4) # top wall
+        self.grid.horz_wall(2, height - 3, width - 4) # bottom wall
+        self.grid.horz_wall(2, int(height/2), width - 4) # middle wall
+
+        # find where to spawn boxes
+        self.potential_box_locations_red = []
+        self.potential_box_locations_green = []
+        self.potential_box_locations_blue = []
+        for i in range(1,width-1):
+            for j in range(1,4):
+                if j != 2 or (i == 1 or i == width-2):
+                    self.grid.set_background(i,j,deliveryRegion1Tile)
+            for j in range(height - 4, height-1):
+                if j != height-3 or (i == 1 or i == width-2):
+                    self.grid.set_background(i,j,deliveryRegion2Tile)
+
+            if i != 1 and i != width-2:
+                if i == 2 or i == 3 or i == 4:
+                    self.potential_box_locations_red.append((i, int(height/2)+1))
+                    self.potential_box_locations_red.append((i, int(height/2)-1))
+                self.potential_box_locations_green.append((i, int(height/2)-1))
+                self.potential_box_locations_blue.append((i, int(height/2)+1))
+
+        # get all possible spawn locations
+        self.potential_spawn_locations_blue = []
+        self.potential_spawn_locations_green = []
+        for i in range(1,width-1):
+            for j in range(1, height-1):
+                # add a buffer around the agent spawn location
+                if abs(i - self.agent_start_pos[0]) <= 1 or abs(j - self.agent_start_pos[1]) <= 1:
+                    continue
+                obj = self.grid.get(i,j)
+                bg = self.grid.get_background(i, j)
+                if obj is None:
+                    if bg is None or bg.color == 'lightblue':
+                        self.potential_spawn_locations_blue.append((i,j))
+                    if bg is None or bg.color == 'lightgreen':
+                        self.potential_spawn_locations_green.append((i,j))
+
+
+
+        self.red_delivery_station = (2,1)
+        self.grid.set_background(*self.red_delivery_station,deliveryStationRLTile)
+
+        self.blue_delivery_station = (8,1)
+        self.grid.set_background(*self.blue_delivery_station,deliveryStation1Tile)
+
+        self.green_delivery_station = (6,height - 2)
+        self.grid.set_background(*self.green_delivery_station,deliveryStation2Tile)
+
+        self.adversaries = {}
+        if self.use_blue_adversary:
+            random_start = self.get_viable_location(self.potential_spawn_locations_blue)
+            self.add_adversary(*random_start, "blue", np.random.randint(low=0, high=4), [DoNothing()])
+            self.add_blue_box()
+
+
+        if self.use_green_adversary:
+            random_start = self.get_viable_location(self.potential_spawn_locations_green)
+            self.add_adversary(*random_start, "green", np.random.randint(low=0, high=4), [DoNothing()])
+            self.add_green_box()
+
+        # learning agent boxes
+        red_random_box_location1 = self.get_viable_location(self.potential_box_locations_red)
+        self.grid.set(*red_random_box_location1,Box("red"))
+        if self.reward_type == 'Dense':
+            self.learning_agent_manager = TaskManager([
+                PickUpObject(red_random_box_location1, self.grid.get(*red_random_box_location1)),
+                PlaceObject(self.red_delivery_station, self.grid.get(*red_random_box_location1)),
+                DoNothing()
+            ])
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+
+        self.mission = "Avoid crashing into the other agents."
+
+    def add_blue_box(self):
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_blue)
+        self.grid.set(*random_box_location1, Box("blue"))
+        self.adversaries["blue"].task_manager.tasks.insert(0,PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+        self.adversaries["blue"].task_manager.tasks.insert(1, PlaceObject(self.blue_delivery_station, self.grid.get(*random_box_location1)))
+
+    def add_green_box(self):
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_green)
+        self.grid.set(*random_box_location1, Box("green"))
+        self.adversaries["green"].task_manager.tasks.insert(0,PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+        self.adversaries["green"].task_manager.tasks.insert(1, PlaceObject(self.green_delivery_station, self.grid.get(*random_box_location1)))
+    def add_red_box(self):
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_red)
+        self.grid.set(*random_box_location1, Box("red"))
+        if self.reward_type == 'Dense':
+            self.learning_agent_manager.tasks.insert(0,PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+            self.learning_agent_manager.tasks.insert(1, PlaceObject(self.red_delivery_station, self.grid.get(*random_box_location1)))
+
+
+    def step(self, action):
+        delete_list = list()
+        for position, box in self.background_boxes.items():
+            if self.grid.get(*position) is None:
+                self.grid.set(*position, box)
+                self.grid.set_background(*position, None)
+                delete_list.append(tuple(position))
+            #else:
+            #    print("Cannot remove box at {}, {} from backgroud".format(*position))
+        for position in delete_list:
+            #print("Removed box at {}, {} from backgroud".format(*position))
+            del self.background_boxes[position]
+        self.reward = 0.0
+
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        # need to augment this just like the agent, and we can hardcode a policy to follow
+        for adversary in self.adversaries.values():
+            blocked_positions = [adv.cur_pos for adv in self.adversaries.values()]
+            agent_pos = self.agent_pos
+            advsersary_action = self.get_adversary_action(adversary)
+            self.move_adversary(adversary, advsersary_action, blocked_positions, agent_pos)
+
+        if self.reward_type == 'Dense':
+            # starting_pos = self.agent_pos
+            best_action = self.learning_agent_manager.get_best_action(self.agent_pos, self.dir_vec, self.carrying, self)
+            if action == best_action:
+                self.reward += 0.1
+            else:
+                self.reward -= 0.1
+
+
+        # reward function and done function are custom
+        reward = self.reward
+
+        if self.reward_type == 'Dense':
+            # current_pos = self.agent_pos
+            # goal_pos = self.learning_agent_manager.tasks[0].obj_position
+            # starting_distance = abs(starting_pos[0] - goal_pos[0]) + abs(starting_pos[1] - goal_pos[1])
+            # ending_distance = abs(current_pos[0] - goal_pos[0]) + abs(current_pos[1] - goal_pos[1])
+            # reward += 0.1 * (starting_distance - ending_distance)
+            # if self.learning_agent_manager.tasks[0].completed(self.agent_pos, self.dir_vec, self.carrying, self):
+            #     self.learning_agent_manager.tasks.pop(0)
+
+
+            if self.learning_agent_manager.tasks[0].completed(self.agent_pos, self.dir_vec, self.carrying, self):
+                self.learning_agent_manager.tasks.pop(0)
+
+        # respawn boxes as needed
+        maybe_red_box = self.grid.get(*self.red_delivery_station)
+        if maybe_red_box and isinstance(maybe_red_box, Box) and maybe_red_box.color == 'red':
+            self.grid.set(*self.red_delivery_station, None)
+            self.add_red_box()
+
+        # respawn green box
+        if self.use_green_adversary:
+            maybe_green_box = self.grid.get(*self.green_delivery_station)
+            if maybe_green_box and isinstance(maybe_green_box, Box) and maybe_green_box.color == 'green':
+                self.grid.set(*self.green_delivery_station, None)
+                self.add_green_box()
+
+        # respawn blue box
+        if self.use_blue_adversary:
+            maybe_blue_box = self.grid.get(*self.blue_delivery_station)
+            if maybe_blue_box and isinstance(maybe_blue_box, Box) and maybe_blue_box.color == 'blue':
+                self.grid.set(*self.blue_delivery_station, None)
+                self.add_blue_box()
+
+
+        return obs, reward, terminated, truncated, info
+
+    def seed(self, seed):
+        np.random.seed(seed)
+        return seed
+
+    def get_adversary_action(self, adversary):
+        return adversary.task_manager.get_best_action(adversary.cur_pos, adversary.dir_vec(), adversary.carrying, self)
+
+    def get_viable_location(self, locations):
+        viable = False
+        while not viable:
+            location = locations[np.random.randint(low=0, high=len(locations))]
+            if self.grid.get(*location) is None and self.agent_pos != location:
+                viable = True
+        return location
+
+    # Moves the adversary according to current policy, code copy pasted from minigrid.step
+    def move_adversary(self, adversary, action, blocked_positions, agent_pos):
+        # fetch current location and forward location
+        cur_pos = adversary.cur_pos
+        current_cell = self.grid.get(*adversary.cur_pos)
+        fwd_pos = cur_pos + adversary.dir_vec()
+        fwd_cell = self.grid.get(*fwd_pos)
+
+
+        if action == self.actions.left:
+            adversary.adversary_dir -= 1
+            if adversary.adversary_dir < 0:
+                adversary.adversary_dir += 4
+
+        # Rotate right
+        elif action == self.actions.right:
+            adversary.adversary_dir = (adversary.adversary_dir + 1) % 4
+
+        # Move forward
+        elif action == self.actions.forward and isinstance(current_cell, SlipperyNorth):
+            print("TODO: actual slippery obs, for now: Teleporting the agent")
+            # TODO also what if blocked, see below
+            adversary.cur_pos = (3, 3)
+
+        elif action == self.actions.forward and not isinstance(current_cell, SlipperyNorth):
+            if tuple(fwd_pos) == agent_pos:
+                self.reward -= self.collision_penalty
+                self.safety_violations_this_episode += 1
+
+
+            elif (fwd_cell is None or fwd_cell.can_overlap()) and not tuple(fwd_pos) in blocked_positions:
+                if isinstance(fwd_cell, Box):
+                    print("set background: ", fwd_cell)
+                    self.grid.set_background(*fwd_pos,fwd_cell)
+                    self.background_boxes[tuple(fwd_pos)] = fwd_cell  # np.array is not hashable
+                adversary.cur_pos = tuple(fwd_pos)
+
+        # Pick up an object
+        elif action == self.actions.pickup:
+            if fwd_cell and fwd_cell.can_pickup():
+                if adversary.carrying is None:
+                    adversary.carrying = fwd_cell
+                    adversary.carrying.cur_pos = np.array([-1, -1])
+                    self.grid.set(fwd_pos[0], fwd_pos[1], None)
+
+        # Drop an object
+        elif action == self.actions.drop:
+            if not fwd_cell and adversary.carrying:
+                self.grid.set(fwd_pos[0], fwd_pos[1], adversary.carrying)
+                adversary.carrying.cur_pos = fwd_pos
+                adversary.carrying = None
+
+        # Toggle/activate an object
+        elif action == self.actions.toggle:
+            if fwd_cell:
+                fwd_cell.toggle(self, fwd_pos)
+
+        # Done action (not used by default)
+        elif action == self.actions.done:
+            pass
+
+        else:
+            raise ValueError(f"Unknown action: {action}")
+
+        # finally update the env with these changes
+        self.grid.set(*cur_pos, None)
+        self.grid.set(*adversary.cur_pos, adversary)
+
+    def get_adversaries(self):
+        return [adversary.color for adversary in self.adversaries.values()]
+
+    def get_property_map(self):
+        return { "blue" : ("SafetyAGBlue", 'concat(<_, PreSafety, gamma={}> <<Blue>> Pmax=? [ G !"BlueOnGreen"  ], <SafetyAGBlue, PreSafety, gamma={}> <<Agent>> Pmax=? [G<{} !"crash" ] );'.format(self.shield_gamma, self.shield_gamma, self.shield_horizon)),
+                 "green": ("SafetyAGGreen", 'concat(<_, PreSafety, gamma={}> <<Green>> Pmax=? [ G !"GreenOnBlue"  ], <SafetyAGGreen, PreSafety, gamma={}> <<Agent>> Pmax=? [G<{} !"crash" ] );'.format(self.shield_gamma, self.shield_gamma, self.shield_horizon))
+                 }
+
+    def get_shield_info(self):
+        adversary_info = {color : ((adv.cur_pos[1], adv.cur_pos[0]), adv.adversary_dir) for (color,adv) in self.adversaries.items()}
+        return self.agent_pos, self.agent_dir, adversary_info
+
+
+
+class DeliveryStationsWithRows(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment is an empty room, and the goal of the agent is to reach the
+    green goal square, which provides a sparse reward. A small penalty is
+    subtracted for the number of steps to reach the goal. This environment is
+    useful, with small rooms, to validate that your RL algorithm works
+    correctly, and with large rooms to experiment with sparse rewards and
+    exploration. The random variants of the environment have the agent starting
+    at a random position for each episode, while the regular variants have the
+    agent always starting in the corner opposite to the goal.
+
+    ### Mission Space
+
+    "get to the green goal square"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-Empty-5x5-v0`
+    - `MiniGrid-Empty-Random-5x5-v0`
+    - `MiniGrid-Empty-6x6-v0`
+    - `MiniGrid-Empty-Random-6x6-v0`
+    - `MiniGrid-Empty-8x8-v0`
+    - `MiniGrid-Empty-16x16-v0`
+
+    """
+
+    def __init__(self, size=8, agent_start_pos=(1, 1), agent_start_dir=0, **kwargs):
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "Delivery packages while avoiding crashes."
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            width=13,
+            height=13,
+            max_steps=400000,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+        for j in range(2, height - 2):
+            if j % 3 == 1: continue
+            if j % 3 == 2: continue
+            self.grid.horz_wall(2, j, width - 4)
+
+
+        self.grid.set(4,1,deliveryStationRLTile)
+        self.grid.set(5,1,deliveryStationRLTile)
+
+        self.grid.set(8,1,deliveryStation1Tile)
+        self.grid.set(9,1,deliveryStation1Tile)
+
+        self.grid.set(5,height - 2,deliveryStation2Tile)
+        self.grid.set(6,height - 2,deliveryStation2Tile)
+
+        self.grid.set(5,7,Adversary(2,"blue"))
+        self.grid.set(3,4,Adversary(1,"green"))
+
+        self.grid.set(3,7,Box("blue"))
+        self.grid.set(4,7,Box("green"))
+        self.grid.set(6,5,Box("blue"))
+        self.grid.set(8,5,Box("green"))
+        self.grid.set(7,5,Box("red"))
+        self.grid.set(8,7,Box("green"))
+        self.grid.set(4,4,Box("red"))
+
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+
+        self.mission = "Delivery packages while avoiding crashes."

gym_minigrid/gym_minigrid/envs/agSmall.py

@@ -0,0 +1,439 @@
+import numpy as np
+from gym import spaces
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, Floor, Adversary, Box, SlipperyNorth, run_BFS_reward
+from gym_minigrid.Task import DoRandom, TaskManager, DoNothing, GoTo, PlaceObject, PickUpObject, Task
+
+from copy import deepcopy
+import sys
+
+deliveryStation1Tile = Floor("blue")
+deliveryStation2Tile = Floor("green")
+deliveryStation3Tile = Floor("purple")
+deliveryStationRLTile = Floor("red")
+deliveryRegion1Tile = Floor("lightblue")
+deliveryRegion2Tile = Floor("lightgreen")
+deliveryRegion3Tile = Floor("lightpurple")
+
+class DeliveryStationsSmall(MiniGridEnv):
+    def __init__(self, agent_start_pos=(1, 1), agent_start_dir=0, highlight:bool=False,
+                 reward_type:str='Dense', number_adversaries:int=3, max_steps=200,
+                 shield_gamma=1.0, shield_horizon=30, collision_penalty=0.1, **kwargs):
+        assert reward_type == 'Sparse' or reward_type == 'Dense', "Must select either 'Sparse' or 'Dense' reward"
+        assert 0 <= number_adversaries <= 3, "Must select either 0, 1, 2 or 3 adversaries"
+
+        self.collision_penalty = collision_penalty
+        self.shield_gamma = shield_gamma
+        self.shield_horizon = shield_horizon
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+        self.number_adversaries = number_adversaries
+        self.reward_type = reward_type
+
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "Avoid crashing into the other agents."
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            width=13,
+            height=13,
+            # Set this to True for maximum speed
+            highlight=highlight,
+            see_through_walls=True,
+            max_steps=max_steps,
+            **kwargs
+        )
+
+
+    def _gen_grid(self, width, height):
+        self.use_purple_adversary = self.number_adversaries >= 3
+        self.use_blue_adversary = self.number_adversaries >= 2
+        self.use_green_adversary = self.number_adversaries >= 1
+
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # add walls
+        self.grid.wall_rect(0, 0, width, height) # outside border
+        self.grid.horz_wall(2, 2, width - 4) # top wall
+        self.grid.horz_wall(2, height - 3, width - 4) # bottom wall
+        #self.grid.horz_wall(2, int(height/2), width - 4) # middle wall
+
+        # find where to spawn boxes
+        self.potential_box_locations_red = []
+        self.potential_box_locations_green = []
+        self.potential_box_locations_blue = []
+        self.potential_box_locations_purple = []
+        for i in range(1,width-1):
+            for j in range(1,4):
+                if j != 2 or (i == 1 or i == width-2):
+                    self.grid.set_background(i,j,deliveryRegion1Tile)
+            for j in range(height - 4, height-1):
+                if j != height-3 or (i == 1 or i == width-2):
+                    self.grid.set_background(i,j,deliveryRegion2Tile)
+
+            #if i != 1 and i != width-2:
+            #    if i == 2 or i == 3 or i == 4:
+            #        self.potential_box_locations_red.append((i, int(height/2)+1))
+            #        self.potential_box_locations_red.append((i, int(height/2)-1))
+
+        self.potential_box_locations_red.append((3, int(height/2)+1))
+        self.potential_box_locations_red.append((3, int(height/2)-1))
+        self.potential_box_locations_red.append((5, int(height/2)-1))
+        self.potential_box_locations_red.append((5, int(height/2)+1))
+        self.potential_box_locations_red.append((4, int(height/2)-1))
+        self.potential_box_locations_red.append((4, int(height/2)+1))
+
+        self.potential_box_locations_blue.append((2, int(height/2)-2))
+        self.potential_box_locations_blue.append((2, int(height/2)-1))
+        self.potential_box_locations_blue.append((2, int(height/2)+1))
+        self.potential_box_locations_blue.append((2, int(height/2)+2))
+        self.potential_box_locations_blue.append((4, int(height/2)-2))
+        self.potential_box_locations_blue.append((4, int(height/2)-1))
+        self.potential_box_locations_blue.append((4, int(height/2)+1))
+        self.potential_box_locations_blue.append((4, int(height/2)+2))
+        self.potential_box_locations_blue.append((6, int(height/2)-2))
+        self.potential_box_locations_blue.append((6, int(height/2)-1))
+        self.potential_box_locations_blue.append((6, int(height/2)+1))
+        self.potential_box_locations_blue.append((6, int(height/2)+2))
+        self.potential_box_locations_blue.append((8, int(height/2)-2))
+        self.potential_box_locations_blue.append((8, int(height/2)-1))
+        self.potential_box_locations_blue.append((8, int(height/2)+1))
+        self.potential_box_locations_blue.append((8, int(height/2)+2))
+
+        self.potential_box_locations_green.append((2, int(height/2)-2))
+        self.potential_box_locations_green.append((2, int(height/2)-1))
+        self.potential_box_locations_green.append((2, int(height/2)+1))
+        self.potential_box_locations_green.append((2, int(height/2)+2))
+        self.potential_box_locations_green.append((4, int(height/2)-2))
+        self.potential_box_locations_green.append((4, int(height/2)-1))
+        self.potential_box_locations_green.append((4, int(height/2)+1))
+        self.potential_box_locations_green.append((4, int(height/2)+2))
+        #self.potential_box_locations_green.append((6, int(height/2)-2))
+        #self.potential_box_locations_green.append((6, int(height/2)-1))
+        #self.potential_box_locations_green.append((6, int(height/2)+1))
+        #self.potential_box_locations_green.append((6, int(height/2)+2))
+        #self.potential_box_locations_green.append((8, int(height/2)-2))
+        #self.potential_box_locations_green.append((8, int(height/2)-1))
+        #self.potential_box_locations_green.append((8, int(height/2)+1))
+        #self.potential_box_locations_green.append((8, int(height/2)+2))
+
+        #self.potential_box_locations_purple.append((2, int(height/2)-2))
+        #self.potential_box_locations_purple.append((2, int(height/2)-1))
+        #self.potential_box_locations_purple.append((2, int(height/2)+1))
+        #self.potential_box_locations_purple.append((2, int(height/2)+2))
+        #self.potential_box_locations_purple.append((4, int(height/2)-2))
+        #self.potential_box_locations_purple.append((4, int(height/2)-1))
+        #self.potential_box_locations_purple.append((4, int(height/2)+1))
+        #self.potential_box_locations_purple.append((4, int(height/2)+2))
+        self.potential_box_locations_purple.append((6, int(height/2)-2))
+        self.potential_box_locations_purple.append((6, int(height/2)-1))
+        self.potential_box_locations_purple.append((6, int(height/2)+1))
+        self.potential_box_locations_purple.append((6, int(height/2)+2))
+        self.potential_box_locations_purple.append((8, int(height/2)-2))
+        self.potential_box_locations_purple.append((8, int(height/2)-1))
+        self.potential_box_locations_purple.append((8, int(height/2)+1))
+        self.potential_box_locations_purple.append((8, int(height/2)+2))
+        # get all possible spawn locations
+        self.potential_spawn_locations_blue = []
+        self.potential_spawn_locations_green = []
+        self.potential_spawn_locations_purple = []
+        for i in range(1,width-1):
+            for j in range(1, height-1):
+                # add a buffer around the agent spawn location
+                if abs(i - self.agent_start_pos[0]) <= 1 or abs(j - self.agent_start_pos[1]) <= 1:
+                    continue
+                obj = self.grid.get(i,j)
+                bg = self.grid.get_background(i, j)
+                if obj is None:
+                    if bg is None or bg.color == 'lightblue':
+                        self.potential_spawn_locations_blue.append((i,j))
+                    if bg is None or bg.color == 'lightgreen':
+                        self.potential_spawn_locations_green.append((i,j))
+                        self.potential_spawn_locations_purple.append((i,j))
+
+
+        self.red_delivery_station = (2,1)
+        self.grid.set_background(*self.red_delivery_station,deliveryStationRLTile)
+
+        self.blue_delivery_station = (8,1)
+        self.grid.set_background(*self.blue_delivery_station,deliveryStation1Tile)
+
+        self.green_delivery_station = (2,height - 2)
+        self.grid.set_background(*self.green_delivery_station,deliveryStation2Tile)
+
+        self.purple_delivery_station = (8,height - 2)
+        self.grid.set_background(*self.purple_delivery_station,deliveryStation3Tile)
+
+        self.adversaries = {}
+        if self.use_blue_adversary:
+            random_start = self.get_viable_location(self.potential_spawn_locations_blue)
+            self.add_adversary(*random_start, "blue", np.random.randint(low=0, high=4), [DoNothing()])
+            self.add_blue_box()
+
+
+        if self.use_green_adversary:
+            random_start = self.get_viable_location(self.potential_spawn_locations_green)
+            self.add_adversary(*random_start, "green", np.random.randint(low=0, high=4), [DoNothing()])
+            self.add_green_box()
+
+        if self.use_purple_adversary:
+            random_start = self.get_viable_location(self.potential_spawn_locations_purple)
+            self.add_adversary(*random_start, "purple", np.random.randint(low=0, high=4), [DoNothing()])
+            self.add_purple_box()
+
+        # learning agent boxes
+        red_random_box_location1 = self.get_viable_location(self.potential_box_locations_red)
+        self.grid.set(*red_random_box_location1,Box("red"))
+        if self.reward_type == 'Dense':
+            self.learning_agent_manager = TaskManager([
+                PickUpObject(red_random_box_location1, self.grid.get(*red_random_box_location1)),
+                PlaceObject((2, 1), self.grid.get(*red_random_box_location1)),
+                DoNothing()
+            ])
+            current_objective = self.learning_agent_manager.tasks[0].obj_position
+            self.bfs_reward = run_BFS_reward(self.grid, current_objective)
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+
+        self.mission = "Avoid crashing into the other agents."
+
+    def add_blue_box(self):
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_blue)
+        self.grid.set(*random_box_location1, Box("blue"))
+        self.adversaries["blue"].task_manager.tasks.insert(0,PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+        self.adversaries["blue"].task_manager.tasks.insert(1, PlaceObject(self.blue_delivery_station, self.grid.get(*random_box_location1)))
+
+    def add_green_box(self):
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_green)
+        self.grid.set(*random_box_location1, Box("green"))
+        self.adversaries["green"].task_manager.tasks.insert(0,PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+        self.adversaries["green"].task_manager.tasks.insert(1, PlaceObject(self.green_delivery_station, self.grid.get(*random_box_location1)))
+
+    def add_purple_box(self):
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_purple)
+        self.grid.set(*random_box_location1, Box("purple"))
+        self.adversaries["purple"].task_manager.tasks.insert(0,PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+        self.adversaries["purple"].task_manager.tasks.insert(1, PlaceObject(self.purple_delivery_station, self.grid.get(*random_box_location1)))
+
+    def add_red_box(self):
+        #sys.exit("adding additional red boxes, even though this should terminate the episode. \n Remove if multiple boxes should be picked up")
+        print("WARN: Adding red box after {} steps.".format(self.step_count))
+        random_box_location1 = self.get_viable_location(self.potential_box_locations_red)
+        self.grid.set(*random_box_location1, Box("red"))
+        if self.reward_type == 'Dense':
+            self.learning_agent_manager.tasks.insert(0, PickUpObject(random_box_location1, self.grid.get(*random_box_location1)))
+            self.learning_agent_manager.tasks.insert(1, PlaceObject(self.red_delivery_station, self.grid.get(*random_box_location1)))
+
+
+    def step(self, action):
+        delete_list = list()
+        for position, box in self.background_boxes.items():
+            if self.grid.get(*position) is None:
+                self.grid.set(*position, box)
+                self.grid.set_background(*position, None)
+                delete_list.append(tuple(position))
+            #else:
+            #    print("Cannot remove box at {}, {} from backgroud".format(*position))
+        for position in delete_list:
+            #print("Removed box at {}, {} from backgroud".format(*position))
+            del self.background_boxes[position]
+        self.reward = 0.0
+
+        obs, reward, terminated, truncated, info = super().step(action)
+        self.violation_deque.appendleft("Agent Movement:\n{}".format(self.printGrid()))
+
+        # need to augment this just like the agent, and we can hardcode a policy to follow
+        for adversary in self.adversaries.values():
+            blocked_positions = [adv.cur_pos for adv in self.adversaries.values()]
+            agent_pos = self.agent_pos
+            advsersary_action = self.get_adversary_action(adversary)
+            self.move_adversary(adversary, advsersary_action, blocked_positions, agent_pos)
+
+        if self.reward_type == 'Dense':
+            #starting_pos = self.agent_pos
+            #best_action = self.learning_agent_manager.get_best_action(self.agent_pos, self.dir_vec, self.carrying, self)
+            #if action == best_action:
+            #    self.reward += 0.1
+            #else:
+            #    self.reward -= 0.1
+            self.reward += self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+
+        # get obs from env
+        #obs, reward, terminated, truncated, info = super().step(action)
+
+        # reward function and done function are custom
+        reward = self.reward
+        #print("Task[0]: {}".format(self.learning_agent_manager.tasks[0]))
+
+        if self.reward_type == 'Dense':
+            # current_pos = self.agent_pos
+            # goal_pos = self.learning_agent_manager.tasks[0].obj_position
+            # starting_distance = abs(starting_pos[0] - goal_pos[0]) + abs(starting_pos[1] - goal_pos[1])
+            # ending_distance = abs(current_pos[0] - goal_pos[0]) + abs(current_pos[1] - goal_pos[1])
+            # reward += 0.1 * (starting_distance - ending_distance)
+            # if self.learning_agent_manager.tasks[0].completed(self.agent_pos, self.dir_vec, self.carrying, self):
+            #     self.learning_agent_manager.tasks.pop(0)
+
+            if self.learning_agent_manager.tasks[0].completed(self.agent_pos, self.dir_vec, self.carrying, self):
+                self.learning_agent_manager.tasks.pop(0)
+                current_objective = self.learning_agent_manager.tasks[0]
+                if hasattr(current_objective, 'obj_position'):
+                    self.bfs_reward = run_BFS_reward(self.grid, current_objective.obj_position)
+
+        # respawn boxes as needed
+        maybe_red_box = self.grid.get(*self.red_delivery_station)
+        if maybe_red_box and isinstance(maybe_red_box, Box) and maybe_red_box.color == 'red':
+            self.grid.set(*self.red_delivery_station, None)
+            self.add_red_box()
+
+        # respawn green box
+        if self.use_green_adversary:
+            maybe_green_box = self.grid.get(*self.green_delivery_station)
+            if maybe_green_box and isinstance(maybe_green_box, Box) and maybe_green_box.color == 'green':
+                self.grid.set(*self.green_delivery_station, None)
+                self.add_green_box()
+
+        # respawn blue box
+        if self.use_blue_adversary:
+            maybe_blue_box = self.grid.get(*self.blue_delivery_station)
+            if maybe_blue_box and isinstance(maybe_blue_box, Box) and maybe_blue_box.color == 'blue':
+                self.grid.set(*self.blue_delivery_station, None)
+                self.add_blue_box()
+
+        # respawn purple box
+        if self.use_purple_adversary:
+            maybe_purple_box = self.grid.get(*self.purple_delivery_station)
+            if maybe_purple_box and isinstance(maybe_purple_box, Box) and maybe_purple_box.color == 'purple':
+                self.grid.set(*self.purple_delivery_station, None)
+                self.add_purple_box()
+
+        #if terminated:
+        #    print("Terminated true @ {}".format(self.step_count))
+        return obs, reward, terminated, truncated, info
+
+    def seed(self, seed):
+        np.random.seed(seed)
+        return seed
+
+    def get_adversary_action(self, adversary):
+        return adversary.task_manager.get_best_action(adversary.cur_pos, adversary.dir_vec(), adversary.carrying, self)
+
+    def get_viable_location(self, locs):
+        tried = list()
+        locations = deepcopy(locs)
+        viable = False
+        counter = 0
+        while not viable:
+            if len(locations) == 0:
+                print("All locations: ", locs)
+                print("Tried: ", tried)
+                print(self.printGrid())
+                sys.exit("No viable locations.")
+            location = locations[np.random.randint(low=0, high=len(locations))]
+            tried.append(location)
+            if self.grid.get(*location) is None and self.agent_pos != location:
+                viable = True
+            else:
+                locations.remove(location)
+
+            counter += 1
+            if counter >= 10:
+                print(location)
+                print(locations)
+                print(self.printGrid())
+                input("")
+        return location
+
+    # Moves the adversary according to current policy, code copy pasted from minigrid.step
+    def move_adversary(self, adversary, action, blocked_positions, agent_pos):
+        # fetch current location and forward location
+        cur_pos = adversary.cur_pos
+        current_cell = self.grid.get(*adversary.cur_pos)
+        fwd_pos = cur_pos + adversary.dir_vec()
+        fwd_cell = self.grid.get(*fwd_pos)
+
+
+        if action == self.actions.left:
+            adversary.adversary_dir -= 1
+            if adversary.adversary_dir < 0:
+                adversary.adversary_dir += 4
+
+        # Rotate right
+        elif action == self.actions.right:
+            adversary.adversary_dir = (adversary.adversary_dir + 1) % 4
+
+        # Move forward
+        elif action == self.actions.forward and isinstance(current_cell, SlipperyNorth):
+            print("TODO: actual slippery obs, for now: Teleporting the agent")
+            # TODO also what if blocked, see below
+            adversary.cur_pos = (3, 3)
+
+        elif action == self.actions.forward and not isinstance(current_cell, SlipperyNorth):
+            if tuple(fwd_pos) == agent_pos:
+                self.reward -= self.collision_penalty
+                self.safety_violations_this_episode += 1
+                #print("Safety Violation @ {},{}, by adversary".format(*fwd_pos))
+                #for g in self.violation_deque:
+                #    print(g)
+                #input("Hit Enter.")
+                self.violation_deque.clear()
+
+
+            elif (fwd_cell is None or fwd_cell.can_overlap()) and not tuple(fwd_pos) in blocked_positions:
+                if isinstance(fwd_cell, Box):
+                    self.grid.set_background(*fwd_pos,fwd_cell)
+                    self.background_boxes[tuple(fwd_pos)] = fwd_cell  # np.array is not hashable
+                adversary.cur_pos = tuple(fwd_pos)
+
+        # Pick up an object
+        elif action == self.actions.pickup:
+            if fwd_cell and fwd_cell.can_pickup():
+                if adversary.carrying is None:
+                    adversary.carrying = fwd_cell
+                    adversary.carrying.cur_pos = np.array([-1, -1])
+                    self.grid.set(fwd_pos[0], fwd_pos[1], None)
+
+        # Drop an object
+        elif action == self.actions.drop:
+            if not fwd_cell and adversary.carrying:
+                self.grid.set(fwd_pos[0], fwd_pos[1], adversary.carrying)
+                adversary.carrying.cur_pos = fwd_pos
+                adversary.carrying = None
+
+        # Toggle/activate an object
+        elif action == self.actions.toggle:
+            if fwd_cell:
+                fwd_cell.toggle(self, fwd_pos)
+
+        # Done action (not used by default)
+        elif action == self.actions.done:
+            pass
+
+        else:
+            raise ValueError(f"Unknown action: {action}")
+
+        # finally update the env with these changes
+
+        self.grid.set(*cur_pos, None)
+        self.grid.set(*adversary.cur_pos, adversary)
+        self.violation_deque.appendleft("{} Movement:\n{}".format(adversary.color, self.printGrid()))
+
+    def get_adversaries(self):
+        return [adversary.color for adversary in self.adversaries.values()]
+
+    def get_property_map(self, epoch_string):
+        return { "blue" : ("SafetyAGBlue", 'concat(<_, PreSafety, gamma={}> <<Blue>> Pmax=? [ G !"BlueOnGreen"  ], <'.format(self.shield_gamma) + epoch_string + 'SafetyAGBlue, PreSafety, gamma={}> <<Agent>> Pmax=? [G<{} !"crash" ] );'.format(self.shield_gamma, self.shield_horizon)),
+                 "green": ("SafetyAGGreen", 'concat(<_, PreSafety, gamma={}> <<Green>> Pmax=? [ G !"GreenOnBlue"  ], <'.format(self.shield_gamma) + epoch_string + 'SafetyAGGreen, PreSafety, gamma={}> <<Agent>> Pmax=? [G<{} !"crash" ] );'.format(self.shield_gamma, self.shield_horizon)),
+                 "purple": ("SafetyAGPurple", 'concat(<_, PreSafety, gamma={}> <<Purple>> Pmax=? [ G !"PurpleOnBlue"  ], <'.format(self.shield_gamma) + epoch_string + 'SafetyAGPurple, PreSafety, gamma={}> <<Agent>> Pmax=? [G<{} !"crash" ] );'.format(self.shield_gamma, self.shield_horizon))
+                 }
+
+    def get_shield_info(self):
+        adversary_info = {color : ((adv.cur_pos[1], adv.cur_pos[0]), adv.adversary_dir) for (color,adv) in self.adversaries.items()}
+        return self.agent_pos, self.agent_dir, adversary_info

gym_minigrid/gym_minigrid/envs/blockedunlockpickup.py

@@ -0,0 +1,103 @@
+from gym_minigrid.minigrid import COLOR_NAMES, Ball, MissionSpace
+from gym_minigrid.roomgrid import RoomGrid
+
+
+class BlockedUnlockPickupEnv(RoomGrid):
+
+    """
+    ### Description
+
+    The agent has to pick up a box which is placed in another room, behind a
+    locked door. The door is also blocked by a ball which the agent has to move
+    before it can unlock the door. Hence, the agent has to learn to move the
+    ball, pick up the key, open the door and pick up the object in the other
+    room. This environment can be solved without relying on language.
+
+    ### Mission Space
+
+    "pick up the {color} {type}"
+
+    {color} is the color of the box. Can be "red", "green", "blue", "purple",
+    "yellow" or "grey".
+
+    {type} is the type of the object. Can be "box" or "key".
+
+    ### Action Space
+
+    | Num | Name         | Action            |
+    |-----|--------------|-------------------|
+    | 0   | left         | Turn left         |
+    | 1   | right        | Turn right        |
+    | 2   | forward      | Move forward      |
+    | 3   | pickup       | Pick up an object |
+    | 4   | drop         | Unused            |
+    | 5   | toggle       | Unused            |
+    | 6   | done         | Unused            |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent picks up the correct box.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-BlockedUnlockPickup-v0`
+
+    """
+
+    def __init__(self, **kwargs):
+        room_size = 6
+        mission_space = MissionSpace(
+            mission_func=lambda color, type: f"pick up the {color} {type}",
+            ordered_placeholders=[COLOR_NAMES, ["box", "key"]],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            num_rows=1,
+            num_cols=2,
+            room_size=room_size,
+            max_steps=16 * room_size**2,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        # Add a box to the room on the right
+        obj, _ = self.add_object(1, 0, kind="box")
+        # Make sure the two rooms are directly connected by a locked door
+        door, pos = self.add_door(0, 0, 0, locked=True)
+        # Block the door with a ball
+        color = self._rand_color()
+        self.grid.set(pos[0] - 1, pos[1], Ball(color))
+        # Add a key to unlock the door
+        self.add_object(0, 0, "key", door.color)
+
+        self.place_agent(0, 0)
+
+        self.obj = obj
+        self.mission = f"pick up the {obj.color} {obj.type}"
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        if action == self.actions.pickup:
+            if self.carrying and self.carrying == self.obj:
+                reward = self._reward()
+                terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/cliffWalking.py

@@ -0,0 +1,76 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, SlipperyNorth, SlipperySouth, SlipperyEast, SlipperyWest, Wall, HeatMapTile, Lava
+from gym_minigrid.policyRepairEnv import PolicyRepairEnv
+from gym_minigrid.minigrid import run_BFS_reward
+
+class CliffWalking(PolicyRepairEnv):
+
+    def __init__(self, size=8, nr_cliffs=1, agent_start_pos=(1, 4), agent_start_dir=0, **kwargs):
+        self.nr_cliffs = nr_cliffs
+        super().__init__(
+            size=size,
+            agent_start_pos=agent_start_pos,
+            agent_start_dir=agent_start_dir,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+        self.grid.horz_wall(1,7)
+
+        #north cliffs
+        lava_pos = [4, 8, 13, 17, 22]
+        for i in range(self.nr_cliffs):
+            self.put_obj(Lava(), lava_pos[i], 1)
+            self.put_obj(SlipperySouth(), lava_pos[i], 2)
+            self.put_obj(SlipperySouth(), lava_pos[i]+1, 2)
+            self.put_obj(SlipperySouth(), lava_pos[i]-1, 2)
+            self.put_obj(SlipperyEast(), lava_pos[i]+1, 1)
+            self.put_obj(SlipperyWest(), lava_pos[i]-1, 1)
+
+        #nsouth cliffs
+        lava_pos = [3, 8, 12, 17, 21]
+        for i in range(self.nr_cliffs):
+            self.put_obj(Lava(), lava_pos[i], 6)
+            self.put_obj(SlipperyNorth(), lava_pos[i], 5)
+            self.put_obj(SlipperyNorth(), lava_pos[i]+1, 5)
+            self.put_obj(SlipperyNorth(), lava_pos[i]-1, 5)
+            self.put_obj(SlipperyEast(), lava_pos[i]+1, 6)
+            self.put_obj(SlipperyWest(), lava_pos[i]-1, 6)
+
+        
+        goalPos = (width-2, 3)
+        self.put_obj(Goal(), *goalPos)
+
+        # Place the agent
+        if not self.agent_pos or (self.agent_pos[0] == -1 and self.agent_pos[1] == -1 and self.agent_dir == -1):
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+
+        self.bfs_reward = run_BFS_reward(self.grid, goalPos)
+        self.mission = "get to the green goal square"
+
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        #bfs reward
+        #reward = self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward -=100
+
+        if self.render_mode == "human":
+            print("step: {}, reward: {}, info: {}\t\t\tthat terminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/columnsMini.py

@@ -0,0 +1,65 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, SlipperyNorth, Wall, HeatMapTile, Lava
+from gym_minigrid.policyRepairEnv import PolicyRepairEnv
+from gym_minigrid.minigrid import run_BFS_reward
+
+class ColumnsMini(PolicyRepairEnv):
+
+    def __init__(self, size=9, agent_start_pos=(1, 1), agent_start_dir=0, **kwargs):
+        super().__init__(
+            size=size,
+            agent_start_pos=agent_start_pos,
+            agent_start_dir=agent_start_dir,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+
+        #self.put_obj(Slippery(), 4, 1)
+        for x in range(1,4):
+            for y in range(2,8):
+                self.put_obj(SlipperyNorth(), x, y)
+
+        self.put_obj(Lava(), 1, 3)
+        self.put_obj(Lava(), 1, 4)
+        self.put_obj(Lava(), 1, 5)
+
+        self.grid.wall_rect(4, 2, 2, 4)
+        goalPos = (6, height - 2)
+        self.put_obj(Goal(), *goalPos)
+
+        # Place the agent
+        if not self.agent_pos or (self.agent_pos[0] == -1 and self.agent_pos[1] == -1 and self.agent_dir == -1):
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+
+        self.bfs_reward = run_BFS_reward(self.grid, goalPos)
+        self.mission = "get to the green goal square"
+
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        #bfs reward
+        #reward = self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward -=100
+
+        if self.render_mode == "human":
+            print("step: {}, reward: {}, info: {}\t\t\tthat terminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/crossing.py

@@ -0,0 +1,166 @@
+import itertools as itt
+
+import numpy as np
+
+from gym_minigrid.minigrid import Goal, Grid, Lava, MiniGridEnv, MissionSpace
+
+
+class CrossingEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    Depending on the `obstacle_type` parameter:
+    - `Lava` - The agent has to reach the green goal square on the other corner
+        of the room while avoiding rivers of deadly lava which terminate the
+        episode in failure. Each lava stream runs across the room either
+        horizontally or vertically, and has a single crossing point which can be
+        safely used; Luckily, a path to the goal is guaranteed to exist. This
+        environment is useful for studying safety and safe exploration.
+    - otherwise - Similar to the `LavaCrossing` environment, the agent has to
+        reach the green goal square on the other corner of the room, however
+        lava is replaced by walls. This MDP is therefore much easier and maybe
+        useful for quickly testing your algorithms.
+
+    ### Mission Space
+    Depending on the `obstacle_type` parameter:
+    - `Lava` - "avoid the lava and get to the green goal square"
+    - otherwise - "find the opening and get to the green goal square"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. The agent falls into lava.
+    3. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    S: size of the map SxS.
+    N: number of valid crossings across lava or walls from the starting position
+    to the goal
+
+    - `Lava` :
+        - `MiniGrid-LavaCrossingS9N1-v0`
+        - `MiniGrid-LavaCrossingS9N2-v0`
+        - `MiniGrid-LavaCrossingS9N3-v0`
+        - `MiniGrid-LavaCrossingS11N5-v0`
+
+    - otherwise :
+        - `MiniGrid-SimpleCrossingS9N1-v0`
+        - `MiniGrid-SimpleCrossingS9N2-v0`
+        - `MiniGrid-SimpleCrossingS9N3-v0`
+        - `MiniGrid-SimpleCrossingS11N5-v0`
+
+    """
+
+    def __init__(self, size=9, num_crossings=1, obstacle_type=Lava, **kwargs):
+        self.num_crossings = num_crossings
+        self.obstacle_type = obstacle_type
+
+        if obstacle_type == Lava:
+            mission_space = MissionSpace(
+                mission_func=lambda: "avoid the lava and get to the green goal square"
+            )
+        else:
+            mission_space = MissionSpace(
+                mission_func=lambda: "find the opening and get to the green goal square"
+            )
+
+        super().__init__(
+            mission_space=mission_space,
+            grid_size=size,
+            max_steps=4 * size * size,
+            # Set this to True for maximum speed
+            see_through_walls=False,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        assert width % 2 == 1 and height % 2 == 1  # odd size
+
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place the agent in the top-left corner
+        self.agent_pos = np.array((1, 1))
+        self.agent_dir = 0
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), width - 2, height - 2)
+
+        # Place obstacles (lava or walls)
+        v, h = object(), object()  # singleton `vertical` and `horizontal` objects
+
+        # Lava rivers or walls specified by direction and position in grid
+        rivers = [(v, i) for i in range(2, height - 2, 2)]
+        rivers += [(h, j) for j in range(2, width - 2, 2)]
+        self.np_random.shuffle(rivers)
+        rivers = rivers[: self.num_crossings]  # sample random rivers
+        rivers_v = sorted(pos for direction, pos in rivers if direction is v)
+        rivers_h = sorted(pos for direction, pos in rivers if direction is h)
+        obstacle_pos = itt.chain(
+            itt.product(range(1, width - 1), rivers_h),
+            itt.product(rivers_v, range(1, height - 1)),
+        )
+        for i, j in obstacle_pos:
+            self.put_obj(self.obstacle_type(), i, j)
+
+        # Sample path to goal
+        path = [h] * len(rivers_v) + [v] * len(rivers_h)
+        self.np_random.shuffle(path)
+
+        # Create openings
+        limits_v = [0] + rivers_v + [height - 1]
+        limits_h = [0] + rivers_h + [width - 1]
+        room_i, room_j = 0, 0
+        for direction in path:
+            if direction is h:
+                i = limits_v[room_i + 1]
+                j = self.np_random.choice(
+                    range(limits_h[room_j] + 1, limits_h[room_j + 1])
+                )
+                room_i += 1
+            elif direction is v:
+                i = self.np_random.choice(
+                    range(limits_v[room_i] + 1, limits_v[room_i + 1])
+                )
+                j = limits_h[room_j + 1]
+                room_j += 1
+            else:
+                assert False
+            self.grid.set(i, j, None)
+
+        self.mission = (
+            "avoid the lava and get to the green goal square"
+            if self.obstacle_type == Lava
+            else "find the opening and get to the green goal square"
+        )

gym_minigrid/gym_minigrid/envs/distshift.py

@@ -0,0 +1,110 @@
+from gym_minigrid.minigrid import Goal, Grid, Lava, MiniGridEnv, MissionSpace
+
+
+class DistShiftEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment is based on one of the DeepMind [AI safety gridworlds]
+    (https://github.com/deepmind/ai-safety-gridworlds). The agent starts in the
+    top-left corner and must reach the goal which is in the top-right corner,
+    but has to avoid stepping into lava on its way. The aim of this environment
+    is to test an agent's ability to generalize. There are two slightly
+    different variants of the environment, so that the agent can be trained on
+    one variant and tested on the other.
+
+    ### Mission Space
+
+    "get to the green goal square"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. The agent falls into lava.
+    3. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-DistShift1-v0`
+    - `MiniGrid-DistShift2-v0`
+
+    """
+
+    def __init__(
+        self,
+        width=9,
+        height=7,
+        agent_start_pos=(1, 1),
+        agent_start_dir=0,
+        strip2_row=2,
+        **kwargs
+    ):
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+        self.goal_pos = (width - 2, 1)
+        self.strip2_row = strip2_row
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "get to the green goal square"
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            width=width,
+            height=height,
+            max_steps=4 * width * height,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), *self.goal_pos)
+
+        # Place the lava rows
+        for i in range(self.width - 6):
+            self.grid.set(3 + i, 1, Lava())
+            self.grid.set(3 + i, self.strip2_row, Lava())
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+
+        self.mission = "get to the green goal square"

gym_minigrid/gym_minigrid/envs/doorkey.py

@@ -0,0 +1,91 @@
+from gym_minigrid.minigrid import Door, Goal, Grid, Key, MiniGridEnv, MissionSpace
+
+
+class DoorKeyEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment has a key that the agent must pick up in order to unlock a
+    goal and then get to the green goal square. This environment is difficult,
+    because of the sparse reward, to solve using classical RL algorithms. It is
+    useful to experiment with curiosity or curriculum learning.
+
+    ### Mission Space
+
+    "use the key to open the door and then get to the goal"
+
+    ### Action Space
+
+    | Num | Name         | Action                    |
+    |-----|--------------|---------------------------|
+    | 0   | left         | Turn left                 |
+    | 1   | right        | Turn right                |
+    | 2   | forward      | Move forward              |
+    | 3   | pickup       | Pick up an object         |
+    | 4   | drop         | Unused                    |
+    | 5   | toggle       | Toggle/activate an object |
+    | 6   | done         | Unused                    |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-DoorKey-5x5-v0`
+    - `MiniGrid-DoorKey-6x6-v0`
+    - `MiniGrid-DoorKey-8x8-v0`
+    - `MiniGrid-DoorKey-16x16-v0`
+
+    """
+
+    def __init__(self, size=8, **kwargs):
+        if "max_steps" not in kwargs:
+            kwargs["max_steps"] = 10 * size * size
+        mission_space = MissionSpace(
+            mission_func=lambda: "use the key to open the door and then get to the goal"
+        )
+        super().__init__(mission_space=mission_space, grid_size=size, **kwargs)
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal in the bottom-right corner
+        self.put_obj(Goal(), width - 2, height - 2)
+
+        # Create a vertical splitting wall
+        splitIdx = self._rand_int(2, width - 2)
+        self.grid.vert_wall(splitIdx, 0)
+
+        # Place the agent at a random position and orientation
+        # on the left side of the splitting wall
+        self.place_agent(size=(splitIdx, height))
+
+        # Place a door in the wall
+        doorIdx = self._rand_int(1, width - 2)
+        self.put_obj(Door("yellow", is_locked=True), splitIdx, doorIdx)
+
+        # Place a yellow key on the left side
+        self.place_obj(obj=Key("yellow"), top=(0, 0), size=(splitIdx, height))
+
+        self.mission = "use the key to open the door and then get to the goal"

gym_minigrid/gym_minigrid/envs/dynamicobstacles.py

@@ -0,0 +1,151 @@
+from operator import add
+
+from gym.spaces import Discrete
+
+from gym_minigrid.minigrid import Ball, Goal, Grid, MiniGridEnv, MissionSpace
+
+
+class DynamicObstaclesEnv(MiniGridEnv):
+    """
+    ### Description
+
+    This environment is an empty room with moving obstacles.
+    The goal of the agent is to reach the green goal square without colliding
+    with any obstacle. A large penalty is subtracted if the agent collides with
+    an obstacle and the episode finishes. This environment is useful to test
+    Dynamic Obstacle Avoidance for mobile robots with Reinforcement Learning in
+    Partial Observability.
+
+    ### Mission Space
+
+    "get to the green goal square"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure. A '-1' penalty is
+    subtracted if the agent collides with an obstacle.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. The agent collides with an obstacle.
+    3. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-Dynamic-Obstacles-5x5-v0`
+    - `MiniGrid-Dynamic-Obstacles-Random-5x5-v0`
+    - `MiniGrid-Dynamic-Obstacles-6x6-v0`
+    - `MiniGrid-Dynamic-Obstacles-Random-6x6-v0`
+    - `MiniGrid-Dynamic-Obstacles-8x8-v0`
+    - `MiniGrid-Dynamic-Obstacles-16x16-v0`
+
+    """
+
+    def __init__(
+        self, size=8, agent_start_pos=(1, 1), agent_start_dir=0, n_obstacles=4, **kwargs
+    ):
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+
+        # Reduce obstacles if there are too many
+        if n_obstacles <= size / 2 + 1:
+            self.n_obstacles = int(n_obstacles)
+        else:
+            self.n_obstacles = int(size / 2)
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "get to the green goal square"
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            grid_size=size,
+            max_steps=4 * size * size,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs
+        )
+        # Allow only 3 actions permitted: left, right, forward
+        self.action_space = Discrete(self.actions.forward + 1)
+        self.reward_range = (-1, 1)
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.grid.set(width - 2, height - 2, Goal())
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+
+        # Place obstacles
+        self.obstacles = []
+        for i_obst in range(self.n_obstacles):
+            self.obstacles.append(Ball())
+            self.place_obj(self.obstacles[i_obst], max_tries=100)
+
+        self.mission = "get to the green goal square"
+
+    def step(self, action):
+        # Invalid action
+        if action >= self.action_space.n:
+            action = 0
+
+        # Check if there is an obstacle in front of the agent
+        front_cell = self.grid.get(*self.front_pos)
+        not_clear = front_cell and front_cell.type != "goal"
+
+        # Update obstacle positions
+        for i_obst in range(len(self.obstacles)):
+            old_pos = self.obstacles[i_obst].cur_pos
+            top = tuple(map(add, old_pos, (-1, -1)))
+
+            try:
+                self.place_obj(
+                    self.obstacles[i_obst], top=top, size=(3, 3), max_tries=100
+                )
+                self.grid.set(old_pos[0], old_pos[1], None)
+            except Exception:
+                pass
+
+        # Update the agent's position/direction
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        # If the agent tried to walk over an obstacle or wall
+        if action == self.actions.forward and not_clear:
+            reward = -1
+            terminated = True
+            return obs, reward, terminated, truncated, info
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/empty.py

@@ -0,0 +1,98 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, Lava
+from gym_minigrid.minigrid import run_BFS_reward
+
+class EmptyEnv(MiniGridEnv):
+    """
+    ### Registered Configurations
+
+    - `MiniGrid-Empty-5x5-v0`
+    - `MiniGrid-Empty-Random-5x5-v0`
+    - `MiniGrid-Empty-6x6-v0`
+    - `MiniGrid-Empty-Random-6x6-v0`
+    - `MiniGrid-Empty-8x8-v0`
+    - `MiniGrid-Empty-16x16-v0`
+
+    """
+
+    def __init__(self, size=8, agent_start_pos=(1, 1), agent_start_dir=0, **kwargs):
+        self.agent_start_pos = agent_start_pos
+        self.agent_start_dir = agent_start_dir
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "get to the green goal square"
+        )
+
+        super().__init__(
+            mission_space=mission_space,
+            grid_size=size,
+            max_steps=4 * size * size,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), width - 2, height - 2)
+        self.put_obj(Lava(), 3, 2)
+        self.put_obj(Lava(), 4, 2)
+        self.put_obj(Lava(), 5, 2)
+
+        self.put_obj(Lava(), 9, 3)
+
+        self.put_obj(Lava(), 5, 4)
+        self.put_obj(Lava(), 6, 4)
+
+        self.put_obj(Lava(), 7, 6)
+        self.put_obj(Lava(), 8, 6)
+
+        self.put_obj(Lava(), 2, 6)
+        self.put_obj(Lava(), 3, 6)
+        self.put_obj(Lava(), 4, 6)
+
+        self.put_obj(Lava(), 6, 8)
+        self.put_obj(Lava(), 7, 8)
+
+        self.put_obj(Lava(), 1, 9)
+        self.put_obj(Lava(), 2, 9)
+        self.put_obj(Lava(), 3, 9)
+        #self.put_obj(Lava(), 3, 6)
+
+        # Place the agent
+        if self.agent_start_pos is not None:
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+        else:
+            self.place_agent()
+
+        self.bfs_reward = run_BFS_reward(self.grid, (width - 2, height - 2))
+        self.mission = "get to the green goal square"
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+
+        obs, reward, terminated, truncated, info = super().step(action)
+        #if fwd_pos[0] == 4 and fwd_pos[1] == 3 and action == self.actions.forward:
+        #    reward += 10
+        #bfs reward
+        reward += 1 * self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward += -10
+
+        if self.render_mode == "human":
+            print("{}, rew: {}, info: {}\tterminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/experimentSlippery.py

@@ -0,0 +1,83 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, SlipperyNorth, Wall, HeatMapTile, Lava
+from gym_minigrid.policyRepairEnv import PolicyRepairEnv
+from gym_minigrid.minigrid import run_BFS_reward
+
+class ExperimentSlippery(PolicyRepairEnv):
+
+    def __init__(self, size=11, agent_start_pos=(1, 1), agent_start_dir=0, **kwargs):
+        super().__init__(
+            size=size,
+            agent_start_pos=agent_start_pos,
+            agent_start_dir=agent_start_dir,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), width - 2, height - 2)
+
+        #Walls in the middle
+        for i in range(3,8):
+            self.put_obj(Wall(),3,i)
+            self.put_obj(Wall(),4,i)
+            self.put_obj(Wall(),5,i)
+            self.put_obj(Wall(),6,i)
+            self.put_obj(Wall(),7,i)
+
+        # Lava north/left
+        self.put_obj(Lava(), 4, 8)
+        self.put_obj(Lava(), 5, 8)
+        self.put_obj(Lava(), 6, 8)
+
+        self.put_obj(Lava(), 8, 4)
+        self.put_obj(Lava(), 8, 5)
+        self.put_obj(Lava(), 8, 6)
+
+        #Slippery Tiles north        
+        self.put_obj(SlipperyNorth(), 3, 8)
+        self.put_obj(SlipperyNorth(), 7, 8)
+        self.put_obj(SlipperyNorth(), 3, 9)
+        self.put_obj(SlipperyNorth(), 4, 9)
+        self.put_obj(SlipperyNorth(), 5, 9)
+        self.put_obj(SlipperyNorth(), 6, 9)
+        self.put_obj(SlipperyNorth(), 7, 9)
+        self.put_obj(SlipperyNorth(), 7, 8)
+
+        # Place the agent
+        if not self.agent_pos or (self.agent_pos[0] == -1 and self.agent_pos[1] == -1 and self.agent_dir == -1):
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+
+        self.bfs_reward = run_BFS_reward(self.grid, (width - 2, height - 2))
+        self.mission = "get to the green goal square"
+
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        #bfs reward
+        reward = self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward -=100
+
+        if self.render_mode == "human":
+            print("step: {}, reward: {}, info: {}\t\t\tthat terminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/fetch.py

@@ -0,0 +1,169 @@
+from gym_minigrid.minigrid import (
+    COLOR_NAMES,
+    Ball,
+    Grid,
+    Key,
+    MiniGridEnv,
+    MissionSpace,
+)
+
+
+class FetchEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment has multiple objects of assorted types and colors. The
+    agent receives a textual string as part of its observation telling it which
+    object to pick up. Picking up the wrong object terminates the episode with
+    zero reward.
+
+    ### Mission Space
+
+    "{syntax} {color} {type}"
+
+    {syntax} is one of the following: "get a", "go get a", "fetch a",
+    "go fetch a", "you must fetch a".
+
+    {color} is the color of the box. Can be "red", "green", "blue", "purple",
+    "yellow" or "grey".
+
+    {type} is the type of the object. Can be "key" or "ball".
+
+    ### Action Space
+
+    | Num | Name         | Action               |
+    |-----|--------------|----------------------|
+    | 0   | left         | Turn left            |
+    | 1   | right        | Turn right           |
+    | 2   | forward      | Move forward         |
+    | 3   | pickup       | Pick up an object    |
+    | 4   | drop         | Unused               |
+    | 5   | toggle       | Unused               |
+    | 6   | done         | Unused               |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent picks up the correct object.
+    2. The agent picks up the wrong object.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    N: number of objects to be generated.
+
+    - `MiniGrid-Fetch-5x5-N2-v0`
+    - `MiniGrid-Fetch-6x6-N2-v0`
+    - `MiniGrid-Fetch-8x8-N3-v0`
+
+    """
+
+    def __init__(self, size=8, numObjs=3, **kwargs):
+        self.numObjs = numObjs
+        self.obj_types = ["key", "ball"]
+
+        MISSION_SYNTAX = [
+            "get a",
+            "go get a",
+            "fetch a",
+            "go fetch a",
+            "you must fetch a",
+        ]
+        self.size = size
+        mission_space = MissionSpace(
+            mission_func=lambda syntax, color, type: f"{syntax} {color} {type}",
+            ordered_placeholders=[MISSION_SYNTAX, COLOR_NAMES, self.obj_types],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=5 * size**2,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.horz_wall(0, 0)
+        self.grid.horz_wall(0, height - 1)
+        self.grid.vert_wall(0, 0)
+        self.grid.vert_wall(width - 1, 0)
+
+        objs = []
+
+        # For each object to be generated
+        while len(objs) < self.numObjs:
+            objType = self._rand_elem(self.obj_types)
+            objColor = self._rand_elem(COLOR_NAMES)
+
+            if objType == "key":
+                obj = Key(objColor)
+            elif objType == "ball":
+                obj = Ball(objColor)
+            else:
+                raise ValueError(
+                    "{} object type given. Object type can only be of values key and ball.".format(
+                        objType
+                    )
+                )
+
+            self.place_obj(obj)
+            objs.append(obj)
+
+        # Randomize the player start position and orientation
+        self.place_agent()
+
+        # Choose a random object to be picked up
+        target = objs[self._rand_int(0, len(objs))]
+        self.targetType = target.type
+        self.targetColor = target.color
+
+        descStr = f"{self.targetColor} {self.targetType}"
+
+        # Generate the mission string
+        idx = self._rand_int(0, 5)
+        if idx == 0:
+            self.mission = "get a %s" % descStr
+        elif idx == 1:
+            self.mission = "go get a %s" % descStr
+        elif idx == 2:
+            self.mission = "fetch a %s" % descStr
+        elif idx == 3:
+            self.mission = "go fetch a %s" % descStr
+        elif idx == 4:
+            self.mission = "you must fetch a %s" % descStr
+        assert hasattr(self, "mission")
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        if self.carrying:
+            if (
+                self.carrying.color == self.targetColor
+                and self.carrying.type == self.targetType
+            ):
+                reward = self._reward()
+                terminated = True
+            else:
+                reward = 0
+                terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/fourrooms.py

@@ -0,0 +1,119 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace
+
+
+class FourRoomsEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    Classic four room reinforcement learning environment. The agent must
+    navigate in a maze composed of four rooms interconnected by 4 gaps in the
+    walls. To obtain a reward, the agent must reach the green goal square. Both
+    the agent and the goal square are randomly placed in any of the four rooms.
+
+    ### Mission Space
+
+    "reach the goal"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-FourRooms-v0`
+
+    """
+
+    def __init__(self, agent_pos=None, goal_pos=None, **kwargs):
+        self._agent_default_pos = agent_pos
+        self._goal_default_pos = goal_pos
+
+        self.size = 19
+        mission_space = MissionSpace(mission_func=lambda: "reach the goal")
+
+        super().__init__(
+            mission_space=mission_space,
+            width=self.size,
+            height=self.size,
+            max_steps=100,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create the grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.horz_wall(0, 0)
+        self.grid.horz_wall(0, height - 1)
+        self.grid.vert_wall(0, 0)
+        self.grid.vert_wall(width - 1, 0)
+
+        room_w = width // 2
+        room_h = height // 2
+
+        # For each row of rooms
+        for j in range(0, 2):
+
+            # For each column
+            for i in range(0, 2):
+                xL = i * room_w
+                yT = j * room_h
+                xR = xL + room_w
+                yB = yT + room_h
+
+                # Bottom wall and door
+                if i + 1 < 2:
+                    self.grid.vert_wall(xR, yT, room_h)
+                    pos = (xR, self._rand_int(yT + 1, yB))
+                    self.grid.set(*pos, None)
+
+                # Bottom wall and door
+                if j + 1 < 2:
+                    self.grid.horz_wall(xL, yB, room_w)
+                    pos = (self._rand_int(xL + 1, xR), yB)
+                    self.grid.set(*pos, None)
+
+        # Randomize the player start position and orientation
+        if self._agent_default_pos is not None:
+            self.agent_pos = self._agent_default_pos
+            self.grid.set(*self._agent_default_pos, None)
+            # assuming random start direction
+            self.agent_dir = self._rand_int(0, 4)
+        else:
+            self.place_agent()
+
+        if self._goal_default_pos is not None:
+            goal = Goal()
+            self.put_obj(goal, *self._goal_default_pos)
+            goal.init_pos, goal.cur_pos = self._goal_default_pos
+        else:
+            self.place_obj(Goal())

gym_minigrid/gym_minigrid/envs/gotodoor.py

@@ -0,0 +1,136 @@
+from gym_minigrid.minigrid import COLOR_NAMES, Door, Grid, MiniGridEnv, MissionSpace
+
+
+class GoToDoorEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment is a room with four doors, one on each wall. The agent
+    receives a textual (mission) string as input, telling it which door to go
+    to, (eg: "go to the red door"). It receives a positive reward for performing
+    the `done` action next to the correct door, as indicated in the mission
+    string.
+
+    ### Mission Space
+
+    "go to the {color} door"
+
+    {color} is the color of the door. Can be "red", "green", "blue", "purple",
+    "yellow" or "grey".
+
+    ### Action Space
+
+    | Num | Name         | Action               |
+    |-----|--------------|----------------------|
+    | 0   | left         | Turn left            |
+    | 1   | right        | Turn right           |
+    | 2   | forward      | Move forward         |
+    | 3   | pickup       | Unused               |
+    | 4   | drop         | Unused               |
+    | 5   | toggle       | Unused               |
+    | 6   | done         | Done completing task |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent stands next the correct door performing the `done` action.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-GoToDoor-5x5-v0`
+    - `MiniGrid-GoToDoor-6x6-v0`
+    - `MiniGrid-GoToDoor-8x8-v0`
+
+    """
+
+    def __init__(self, size=5, **kwargs):
+        assert size >= 5
+        self.size = size
+        mission_space = MissionSpace(
+            mission_func=lambda color: f"go to the {color} door",
+            ordered_placeholders=[COLOR_NAMES],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=5 * size**2,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        # Create the grid
+        self.grid = Grid(width, height)
+
+        # Randomly vary the room width and height
+        width = self._rand_int(5, width + 1)
+        height = self._rand_int(5, height + 1)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Generate the 4 doors at random positions
+        doorPos = []
+        doorPos.append((self._rand_int(2, width - 2), 0))
+        doorPos.append((self._rand_int(2, width - 2), height - 1))
+        doorPos.append((0, self._rand_int(2, height - 2)))
+        doorPos.append((width - 1, self._rand_int(2, height - 2)))
+
+        # Generate the door colors
+        doorColors = []
+        while len(doorColors) < len(doorPos):
+            color = self._rand_elem(COLOR_NAMES)
+            if color in doorColors:
+                continue
+            doorColors.append(color)
+
+        # Place the doors in the grid
+        for idx, pos in enumerate(doorPos):
+            color = doorColors[idx]
+            self.grid.set(*pos, Door(color))
+
+        # Randomize the agent start position and orientation
+        self.place_agent(size=(width, height))
+
+        # Select a random target door
+        doorIdx = self._rand_int(0, len(doorPos))
+        self.target_pos = doorPos[doorIdx]
+        self.target_color = doorColors[doorIdx]
+
+        # Generate the mission string
+        self.mission = "go to the %s door" % self.target_color
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        ax, ay = self.agent_pos
+        tx, ty = self.target_pos
+
+        # Don't let the agent open any of the doors
+        if action == self.actions.toggle:
+            terminated = True
+
+        # Reward performing done action in front of the target door
+        if action == self.actions.done:
+            if (ax == tx and abs(ay - ty) == 1) or (ay == ty and abs(ax - tx) == 1):
+                reward = self._reward()
+            terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/gotoobject.py

@@ -0,0 +1,104 @@
+from gym_minigrid.minigrid import (
+    COLOR_NAMES,
+    Ball,
+    Box,
+    Grid,
+    Key,
+    MiniGridEnv,
+    MissionSpace,
+)
+
+
+class GoToObjectEnv(MiniGridEnv):
+    """
+    Environment in which the agent is instructed to go to a given object
+    named using an English text string
+    """
+
+    def __init__(self, size=6, numObjs=2, **kwargs):
+        self.numObjs = numObjs
+        self.size = size
+        # Types of objects to be generated
+        self.obj_types = ["key", "ball", "box"]
+
+        mission_space = MissionSpace(
+            mission_func=lambda color, type: f"go to the {color} {type}",
+            ordered_placeholders=[COLOR_NAMES, self.obj_types],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=5 * size**2,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Types and colors of objects we can generate
+        types = ["key", "ball", "box"]
+
+        objs = []
+        objPos = []
+
+        # Until we have generated all the objects
+        while len(objs) < self.numObjs:
+            objType = self._rand_elem(types)
+            objColor = self._rand_elem(COLOR_NAMES)
+
+            # If this object already exists, try again
+            if (objType, objColor) in objs:
+                continue
+
+            if objType == "key":
+                obj = Key(objColor)
+            elif objType == "ball":
+                obj = Ball(objColor)
+            elif objType == "box":
+                obj = Box(objColor)
+            else:
+                raise ValueError(
+                    "{} object type given. Object type can only be of values key, ball and box.".format(
+                        objType
+                    )
+                )
+
+            pos = self.place_obj(obj)
+            objs.append((objType, objColor))
+            objPos.append(pos)
+
+        # Randomize the agent start position and orientation
+        self.place_agent()
+
+        # Choose a random object to be picked up
+        objIdx = self._rand_int(0, len(objs))
+        self.targetType, self.target_color = objs[objIdx]
+        self.target_pos = objPos[objIdx]
+
+        descStr = f"{self.target_color} {self.targetType}"
+        self.mission = "go to the %s" % descStr
+        # print(self.mission)
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        ax, ay = self.agent_pos
+        tx, ty = self.target_pos
+
+        # Toggle/pickup action terminates the episode
+        if action == self.actions.toggle:
+            terminated = True
+
+        # Reward performing the done action next to the target object
+        if action == self.actions.done:
+            if abs(ax - tx) <= 1 and abs(ay - ty) <= 1:
+                reward = self._reward()
+            terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/guidedRLExperiments.py

@@ -0,0 +1,91 @@
+from gym_minigrid.minigrid import COLOR_NAMES, Ball, MissionSpace
+from gym_minigrid.roomgrid import RoomGrid
+
+
+class TwoRooms(RoomGrid):
+
+    """
+    ### Description
+
+    ### Mission Space
+
+
+    ### Action Space
+
+    | Num | Name         | Action            |
+    |-----|--------------|-------------------|
+    | 0   | left         | Turn left         |
+    | 1   | right        | Turn right        |
+    | 2   | forward      | Move forward      |
+    | 3   | pickup       | Pick up an object |
+    | 4   | drop         | Unused            |
+    | 5   | toggle       | Unused            |
+    | 6   | done         | Unused            |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent picks up the correct box.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-BlockedUnlockPickup-v0`
+
+    """
+
+    def __init__(self, **kwargs):
+        room_size = 6
+        mission_space = MissionSpace(
+            mission_func=lambda color, type: f"pick up the {color} {type}",
+            ordered_placeholders=[COLOR_NAMES, ["box", "key"]],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            num_rows=1,
+            num_cols=2,
+            room_size=room_size,
+            max_steps=16 * room_size**2,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        # Add a box to the room on the right
+        obj, _ = self.add_object(1, 0, kind="box")
+        # Make sure the two rooms are directly connected by a locked door
+        door, pos = self.add_door(0, 0, 0, locked=True)
+        # Block the door with a ball
+        color = self._rand_color()
+        self.grid.set(pos[0] - 1, pos[1], Ball(color))
+        # Add a key to unlock the door
+        self.add_object(0, 0, "key", door.color)
+
+        self.place_agent(0, 0)
+
+        self.obj = obj
+        self.mission = f"pick up the {obj.color} {obj.type}"
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        if action == self.actions.pickup:
+            if self.carrying and self.carrying == self.obj:
+                reward = self._reward()
+                terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/keycorridor.py

@@ -0,0 +1,119 @@
+from gym_minigrid.minigrid import COLOR_NAMES, MissionSpace
+from gym_minigrid.roomgrid import RoomGrid
+
+
+class KeyCorridorEnv(RoomGrid):
+
+    """
+    ### Description
+
+    This environment is similar to the locked room environment, but there are
+    multiple registered environment configurations of increasing size,
+    making it easier to use curriculum learning to train an agent to solve it.
+    The agent has to pick up an object which is behind a locked door. The key is
+    hidden in another room, and the agent has to explore the environment to find
+    it. The mission string does not give the agent any clues as to where the
+    key is placed. This environment can be solved without relying on language.
+
+    ### Mission Space
+
+    "pick up the {color} {obj_type}"
+
+    {color} is the color of the object. Can be "red", "green", "blue", "purple",
+    "yellow" or "grey".
+
+    {type} is the type of the object. Can be "ball" or "key".
+
+    ### Action Space
+
+    | Num | Name         | Action            |
+    |-----|--------------|-------------------|
+    | 0   | left         | Turn left         |
+    | 1   | right        | Turn right        |
+    | 2   | forward      | Move forward      |
+    | 3   | pickup       | Pick up an object |
+    | 4   | drop         | Unused            |
+    | 5   | toggle       | Unused            |
+    | 6   | done         | Unused            |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent picks up the correct object.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    S: room size.
+    R: Number of rows.
+
+    - `MiniGrid-KeyCorridorS3R1-v0`
+    - `MiniGrid-KeyCorridorS3R2-v0`
+    - `MiniGrid-KeyCorridorS3R3-v0`
+    - `MiniGrid-KeyCorridorS4R3-v0`
+    - `MiniGrid-KeyCorridorS5R3-v0`
+    - `MiniGrid-KeyCorridorS6R3-v0`
+
+    """
+
+    def __init__(self, num_rows=3, obj_type="ball", room_size=6, **kwargs):
+        self.obj_type = obj_type
+        mission_space = MissionSpace(
+            mission_func=lambda color: f"pick up the {color} {obj_type}",
+            ordered_placeholders=[COLOR_NAMES],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            room_size=room_size,
+            num_rows=num_rows,
+            max_steps=30 * room_size**2,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        # Connect the middle column rooms into a hallway
+        for j in range(1, self.num_rows):
+            self.remove_wall(1, j, 3)
+
+        # Add a locked door on the bottom right
+        # Add an object behind the locked door
+        room_idx = self._rand_int(0, self.num_rows)
+        door, _ = self.add_door(2, room_idx, 2, locked=True)
+        obj, _ = self.add_object(2, room_idx, kind=self.obj_type)
+
+        # Add a key in a random room on the left side
+        self.add_object(0, self._rand_int(0, self.num_rows), "key", door.color)
+
+        # Place the agent in the middle
+        self.place_agent(1, self.num_rows // 2)
+
+        # Make sure all rooms are accessible
+        self.connect_all()
+
+        self.obj = obj
+        self.mission = f"pick up the {obj.color} {obj.type}"
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        if action == self.actions.pickup:
+            if self.carrying and self.carrying == self.obj:
+                reward = self._reward()
+                terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/lavaObstaclesMini.py

@@ -0,0 +1,79 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, Lava, Wall, HeatMapTile
+from gym_minigrid.policyRepairEnv import PolicyRepairEnv
+from gym_minigrid.minigrid import run_BFS_reward
+
+class LavaObstaclesMini(PolicyRepairEnv):
+
+    def __init__(self, size=11, agent_start_pos=(1, 1), agent_start_dir=0, **kwargs):
+        super().__init__(
+            size=size,
+            agent_start_pos=agent_start_pos,
+            agent_start_dir=agent_start_dir,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), width - 2, height - 2)
+        self.put_obj(Lava(), 3, 2)
+        self.put_obj(Lava(), 4, 2)
+        self.put_obj(Lava(), 5, 2)
+
+        self.put_obj(Lava(), 9, 3)
+
+        self.put_obj(Lava(), 5, 4)
+        self.put_obj(Lava(), 6, 4)
+
+        self.put_obj(Lava(), 7, 6)
+        self.put_obj(Lava(), 8, 6)
+
+        self.put_obj(Lava(), 2, 6)
+        self.put_obj(Lava(), 3, 6)
+        self.put_obj(Lava(), 4, 6)
+
+        self.put_obj(Lava(), 6, 8)
+        self.put_obj(Lava(), 7, 8)
+
+        self.put_obj(Lava(), 1, 9)
+        self.put_obj(Lava(), 2, 9)
+        self.put_obj(Lava(), 3, 9)
+        #self.put_obj(Lava(), 3, 6)
+
+        # Place the agent
+        if not self.agent_pos or (self.agent_pos[0] == -1 and self.agent_pos[1] == -1 and self.agent_dir == -1):
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+
+        self.bfs_reward = run_BFS_reward(self.grid, (width - 2, height - 2))
+        self.mission = "get to the green goal square"
+
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        #bfs reward
+        reward = self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward -=100
+
+        if self.render_mode == "human":
+            print("step: {}, reward: {}, info: {}\t\t\tthat terminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/lavaSymmetricMini.py

@@ -0,0 +1,58 @@
+from gym_minigrid.minigrid import Goal, Grid, MiniGridEnv, MissionSpace, Lava, Wall, HeatMapTile
+from gym_minigrid.policyRepairEnv import PolicyRepairEnv
+from gym_minigrid.minigrid import run_BFS_reward
+
+class LavaSymmetricMini(PolicyRepairEnv):
+
+    def __init__(self, size=6, agent_start_pos=(4, 4), agent_start_dir=0, **kwargs):
+        super().__init__(
+            size=size,
+            agent_start_pos=agent_start_pos,
+            agent_start_dir=agent_start_dir,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place a goal square in the bottom-right corner
+        self.put_obj(Goal(), 1, 1)
+        self.put_obj(Lava(), 2, 4)
+        self.put_obj(Lava(), 4, 2)
+        #self.put_obj(Lava(), 2, 2)
+        # Place the agent
+        if not self.agent_pos or (self.agent_pos[0] == -1 and self.agent_pos[1] == -1 and self.agent_dir == -1):
+            self.agent_pos = self.agent_start_pos
+            self.agent_dir = self.agent_start_dir
+
+        self.bfs_reward = run_BFS_reward(self.grid, (width - 2, height - 2))
+        self.mission = "get to the green goal square"
+
+
+
+    def step(self, action):
+        # Get the position in front of the agent
+        fwd_pos = self.front_pos
+        # Get the contents of the cell in front of the agent
+        fwd_cell = self.grid.get(*fwd_pos)
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        #bfs reward
+        reward = self.bfs_reward[self.agent_pos[0] + self.grid.width * self.agent_pos[1]]
+        #negative reward for stepping in lava, positive reward for reaching goal
+        if action == self.actions.forward:
+            if fwd_cell is not None and fwd_cell.type == "goal":
+                reward += 100
+            elif fwd_cell is not None and fwd_cell.type == "lava":
+                reward -=100
+
+        if self.render_mode == "human":
+            print("step: {}, reward: {}, info: {}\t\t\tthat terminated: {}".format(self.total_timesteps, reward, info, terminated or truncated))
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/lavagap.py

@@ -0,0 +1,122 @@
+import numpy as np
+
+from gym_minigrid.minigrid import Goal, Grid, Lava, MiniGridEnv, MissionSpace
+
+
+class LavaGapEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    The agent has to reach the green goal square at the opposite corner of the
+    room, and must pass through a narrow gap in a vertical strip of deadly lava.
+    Touching the lava terminate the episode with a zero reward. This environment
+    is useful for studying safety and safe exploration.
+
+    ### Mission Space
+
+    Depending on the `obstacle_type` parameter:
+    - `Lava`: "avoid the lava and get to the green goal square"
+    - otherwise: "find the opening and get to the green goal square"
+
+    ### Action Space
+
+    | Num | Name         | Action       |
+    |-----|--------------|--------------|
+    | 0   | left         | Turn left    |
+    | 1   | right        | Turn right   |
+    | 2   | forward      | Move forward |
+    | 3   | pickup       | Unused       |
+    | 4   | drop         | Unused       |
+    | 5   | toggle       | Unused       |
+    | 6   | done         | Unused       |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. The agent falls into lava.
+    3. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    S: size of map SxS.
+
+    - `MiniGrid-LavaGapS5-v0`
+    - `MiniGrid-LavaGapS6-v0`
+    - `MiniGrid-LavaGapS7-v0`
+
+    """
+
+    def __init__(self, size, obstacle_type=Lava, **kwargs):
+        self.obstacle_type = obstacle_type
+        self.size = size
+
+        if obstacle_type == Lava:
+            mission_space = MissionSpace(
+                mission_func=lambda: "avoid the lava and get to the green goal square"
+            )
+        else:
+            mission_space = MissionSpace(
+                mission_func=lambda: "find the opening and get to the green goal square"
+            )
+
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=4 * size * size,
+            # Set this to True for maximum speed
+            see_through_walls=False,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        assert width >= 5 and height >= 5
+
+        # Create an empty grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.wall_rect(0, 0, width, height)
+
+        # Place the agent in the top-left corner
+        self.agent_pos = np.array((1, 1))
+        self.agent_dir = 0
+
+        # Place a goal square in the bottom-right corner
+        self.goal_pos = np.array((width - 2, height - 2))
+        self.put_obj(Goal(), *self.goal_pos)
+
+        # Generate and store random gap position
+        self.gap_pos = np.array(
+            (
+                self._rand_int(2, width - 2),
+                self._rand_int(1, height - 1),
+            )
+        )
+
+        # Place the obstacle wall
+        self.grid.vert_wall(self.gap_pos[0], 1, height - 2, self.obstacle_type)
+
+        # Put a hole in the wall
+        self.grid.set(*self.gap_pos, None)
+
+        self.mission = (
+            "avoid the lava and get to the green goal square"
+            if self.obstacle_type == Lava
+            else "find the opening and get to the green goal square"
+        )

gym_minigrid/gym_minigrid/envs/lockedroom.py

@@ -0,0 +1,167 @@
+from gym_minigrid.minigrid import (
+    COLOR_NAMES,
+    Door,
+    Goal,
+    Grid,
+    Key,
+    MiniGridEnv,
+    MissionSpace,
+    Wall,
+)
+
+
+class LockedRoom:
+    def __init__(self, top, size, doorPos):
+        self.top = top
+        self.size = size
+        self.doorPos = doorPos
+        self.color = None
+        self.locked = False
+
+    def rand_pos(self, env):
+        topX, topY = self.top
+        sizeX, sizeY = self.size
+        return env._rand_pos(topX + 1, topX + sizeX - 1, topY + 1, topY + sizeY - 1)
+
+
+class LockedRoomEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    The environment has six rooms, one of which is locked. The agent receives
+    a textual mission string as input, telling it which room to go to in order
+    to get the key that opens the locked room. It then has to go into the locked
+    room in order to reach the final goal. This environment is extremely
+    difficult to solve with vanilla reinforcement learning alone.
+
+    ### Mission Space
+
+    "get the {lockedroom_color} key from the {keyroom_color} room, unlock the {door_color} door and go to the goal"
+
+    {lockedroom_color}, {keyroom_color}, and {door_color} can be "red", "green",
+    "blue", "purple", "yellow" or "grey".
+
+    ### Action Space
+
+    | Num | Name         | Action                    |
+    |-----|--------------|---------------------------|
+    | 0   | left         | Turn left                 |
+    | 1   | right        | Turn right                |
+    | 2   | forward      | Move forward              |
+    | 3   | pickup       | Pick up an object         |
+    | 4   | drop         | Unused                    |
+    | 5   | toggle       | Toggle/activate an object |
+    | 6   | done         | Unused                    |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    - `MiniGrid-LockedRoom-v0`
+
+    """
+
+    def __init__(self, size=19, **kwargs):
+        self.size = size
+        mission_space = MissionSpace(
+            mission_func=lambda lockedroom_color, keyroom_color, door_color: f"get the {lockedroom_color} key from the {keyroom_color} room, unlock the {door_color} door and go to the goal",
+            ordered_placeholders=[COLOR_NAMES] * 3,
+        )
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=10 * size,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        # Create the grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        for i in range(0, width):
+            self.grid.set(i, 0, Wall())
+            self.grid.set(i, height - 1, Wall())
+        for j in range(0, height):
+            self.grid.set(0, j, Wall())
+            self.grid.set(width - 1, j, Wall())
+
+        # Hallway walls
+        lWallIdx = width // 2 - 2
+        rWallIdx = width // 2 + 2
+        for j in range(0, height):
+            self.grid.set(lWallIdx, j, Wall())
+            self.grid.set(rWallIdx, j, Wall())
+
+        self.rooms = []
+
+        # Room splitting walls
+        for n in range(0, 3):
+            j = n * (height // 3)
+            for i in range(0, lWallIdx):
+                self.grid.set(i, j, Wall())
+            for i in range(rWallIdx, width):
+                self.grid.set(i, j, Wall())
+
+            roomW = lWallIdx + 1
+            roomH = height // 3 + 1
+            self.rooms.append(LockedRoom((0, j), (roomW, roomH), (lWallIdx, j + 3)))
+            self.rooms.append(
+                LockedRoom((rWallIdx, j), (roomW, roomH), (rWallIdx, j + 3))
+            )
+
+        # Choose one random room to be locked
+        lockedRoom = self._rand_elem(self.rooms)
+        lockedRoom.locked = True
+        goalPos = lockedRoom.rand_pos(self)
+        self.grid.set(*goalPos, Goal())
+
+        # Assign the door colors
+        colors = set(COLOR_NAMES)
+        for room in self.rooms:
+            color = self._rand_elem(sorted(colors))
+            colors.remove(color)
+            room.color = color
+            if room.locked:
+                self.grid.set(*room.doorPos, Door(color, is_locked=True))
+            else:
+                self.grid.set(*room.doorPos, Door(color))
+
+        # Select a random room to contain the key
+        while True:
+            keyRoom = self._rand_elem(self.rooms)
+            if keyRoom != lockedRoom:
+                break
+        keyPos = keyRoom.rand_pos(self)
+        self.grid.set(*keyPos, Key(lockedRoom.color))
+
+        # Randomize the player start position and orientation
+        self.agent_pos = self.place_agent(
+            top=(lWallIdx, 0), size=(rWallIdx - lWallIdx, height)
+        )
+
+        # Generate the mission string
+        self.mission = (
+            "get the %s key from the %s room, "
+            "unlock the %s door and "
+            "go to the goal"
+        ) % (lockedRoom.color, keyRoom.color, lockedRoom.color)

gym_minigrid/gym_minigrid/envs/memory.py

@@ -0,0 +1,151 @@
+import numpy as np
+
+from gym_minigrid.minigrid import Ball, Grid, Key, MiniGridEnv, MissionSpace, Wall
+
+
+class MemoryEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment is a memory test. The agent starts in a small room where it
+    sees an object. It then has to go through a narrow hallway which ends in a
+    split. At each end of the split there is an object, one of which is the same
+    as the object in the starting room. The agent has to remember the initial
+    object, and go to the matching object at split.
+
+    ### Mission Space
+
+    "go to the matching object at the end of the hallway"
+
+    ### Action Space
+
+    | Num | Name         | Action                    |
+    |-----|--------------|---------------------------|
+    | 0   | left         | Turn left                 |
+    | 1   | right        | Turn right                |
+    | 2   | forward      | Move forward              |
+    | 3   | pickup       | Pick up an object         |
+    | 4   | drop         | Unused                    |
+    | 5   | toggle       | Toggle/activate an object |
+    | 6   | done         | Unused                    |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the correct matching object.
+    2. The agent reaches the wrong matching object.
+    3. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    S: size of map SxS.
+
+    - `MiniGrid-MemoryS17Random-v0`
+    - `MiniGrid-MemoryS13Random-v0`
+    - `MiniGrid-MemoryS13-v0`
+    - `MiniGrid-MemoryS11-v0`
+
+    """
+
+    def __init__(self, size=8, random_length=False, **kwargs):
+        self.size = size
+        self.random_length = random_length
+        mission_space = MissionSpace(
+            mission_func=lambda: "go to the matching object at the end of the hallway"
+        )
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=5 * size**2,
+            # Set this to True for maximum speed
+            see_through_walls=False,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.horz_wall(0, 0)
+        self.grid.horz_wall(0, height - 1)
+        self.grid.vert_wall(0, 0)
+        self.grid.vert_wall(width - 1, 0)
+
+        assert height % 2 == 1
+        upper_room_wall = height // 2 - 2
+        lower_room_wall = height // 2 + 2
+        if self.random_length:
+            hallway_end = self._rand_int(4, width - 2)
+        else:
+            hallway_end = width - 3
+
+        # Start room
+        for i in range(1, 5):
+            self.grid.set(i, upper_room_wall, Wall())
+            self.grid.set(i, lower_room_wall, Wall())
+        self.grid.set(4, upper_room_wall + 1, Wall())
+        self.grid.set(4, lower_room_wall - 1, Wall())
+
+        # Horizontal hallway
+        for i in range(5, hallway_end):
+            self.grid.set(i, upper_room_wall + 1, Wall())
+            self.grid.set(i, lower_room_wall - 1, Wall())
+
+        # Vertical hallway
+        for j in range(0, height):
+            if j != height // 2:
+                self.grid.set(hallway_end, j, Wall())
+            self.grid.set(hallway_end + 2, j, Wall())
+
+        # Fix the player's start position and orientation
+        self.agent_pos = np.array((self._rand_int(1, hallway_end + 1), height // 2))
+        self.agent_dir = 0
+
+        # Place objects
+        start_room_obj = self._rand_elem([Key, Ball])
+        self.grid.set(1, height // 2 - 1, start_room_obj("green"))
+
+        other_objs = self._rand_elem([[Ball, Key], [Key, Ball]])
+        pos0 = (hallway_end + 1, height // 2 - 2)
+        pos1 = (hallway_end + 1, height // 2 + 2)
+        self.grid.set(*pos0, other_objs[0]("green"))
+        self.grid.set(*pos1, other_objs[1]("green"))
+
+        # Choose the target objects
+        if start_room_obj == other_objs[0]:
+            self.success_pos = (pos0[0], pos0[1] + 1)
+            self.failure_pos = (pos1[0], pos1[1] - 1)
+        else:
+            self.success_pos = (pos1[0], pos1[1] - 1)
+            self.failure_pos = (pos0[0], pos0[1] + 1)
+
+        self.mission = "go to the matching object at the end of the hallway"
+
+    def step(self, action):
+        if action == self.Actions.pickup:
+            action = self.Actions.toggle
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        if tuple(self.agent_pos) == self.success_pos:
+            reward = self._reward()
+            terminated = True
+        if tuple(self.agent_pos) == self.failure_pos:
+            reward = 0
+            terminated = True
+
+        return obs, reward, terminated, truncated, info

gym_minigrid/gym_minigrid/envs/multiroom.py

@@ -0,0 +1,271 @@
+from gym_minigrid.minigrid import (
+    COLOR_NAMES,
+    Door,
+    Goal,
+    Grid,
+    MiniGridEnv,
+    MissionSpace,
+    Wall,
+)
+
+
+class MultiRoom:
+    def __init__(self, top, size, entryDoorPos, exitDoorPos):
+        self.top = top
+        self.size = size
+        self.entryDoorPos = entryDoorPos
+        self.exitDoorPos = exitDoorPos
+
+
+class MultiRoomEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    This environment has a series of connected rooms with doors that must be
+    opened in order to get to the next room. The final room has the green goal
+    square the agent must get to. This environment is extremely difficult to
+    solve using RL alone. However, by gradually increasing the number of rooms
+    and building a curriculum, the environment can be solved.
+
+    ### Mission Space
+
+    "traverse the rooms to get to the goal"
+
+    ### Action Space
+
+    | Num | Name         | Action                    |
+    |-----|--------------|---------------------------|
+    | 0   | left         | Turn left                 |
+    | 1   | right        | Turn right                |
+    | 2   | forward      | Move forward              |
+    | 3   | pickup       | Unused                    |
+    | 4   | drop         | Unused                    |
+    | 5   | toggle       | Toggle/activate an object |
+    | 6   | done         | Unused                    |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent reaches the goal.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    S: size of map SxS.
+    N: number of rooms.
+
+    - `MiniGrid-MultiRoom-N2-S4-v0` (two small rooms)
+    - `MiniGrid-MultiRoom-N4-S5-v0` (four rooms)
+    - `MiniGrid-MultiRoom-N6-v0` (six rooms)
+
+    """
+
+    def __init__(self, minNumRooms, maxNumRooms, maxRoomSize=10, **kwargs):
+        assert minNumRooms > 0
+        assert maxNumRooms >= minNumRooms
+        assert maxRoomSize >= 4
+
+        self.minNumRooms = minNumRooms
+        self.maxNumRooms = maxNumRooms
+        self.maxRoomSize = maxRoomSize
+
+        self.rooms = []
+
+        mission_space = MissionSpace(
+            mission_func=lambda: "traverse the rooms to get to the goal"
+        )
+
+        self.size = 25
+
+        super().__init__(
+            mission_space=mission_space,
+            width=self.size,
+            height=self.size,
+            max_steps=self.maxNumRooms * 20,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        roomList = []
+
+        # Choose a random number of rooms to generate
+        numRooms = self._rand_int(self.minNumRooms, self.maxNumRooms + 1)
+
+        while len(roomList) < numRooms:
+            curRoomList = []
+
+            entryDoorPos = (self._rand_int(0, width - 2), self._rand_int(0, width - 2))
+
+            # Recursively place the rooms
+            self._placeRoom(
+                numRooms,
+                roomList=curRoomList,
+                minSz=4,
+                maxSz=self.maxRoomSize,
+                entryDoorWall=2,
+                entryDoorPos=entryDoorPos,
+            )
+
+            if len(curRoomList) > len(roomList):
+                roomList = curRoomList
+
+        # Store the list of rooms in this environment
+        assert len(roomList) > 0
+        self.rooms = roomList
+
+        # Create the grid
+        self.grid = Grid(width, height)
+        wall = Wall()
+
+        prevDoorColor = None
+
+        # For each room
+        for idx, room in enumerate(roomList):
+
+            topX, topY = room.top
+            sizeX, sizeY = room.size
+
+            # Draw the top and bottom walls
+            for i in range(0, sizeX):
+                self.grid.set(topX + i, topY, wall)
+                self.grid.set(topX + i, topY + sizeY - 1, wall)
+
+            # Draw the left and right walls
+            for j in range(0, sizeY):
+                self.grid.set(topX, topY + j, wall)
+                self.grid.set(topX + sizeX - 1, topY + j, wall)
+
+            # If this isn't the first room, place the entry door
+            if idx > 0:
+                # Pick a door color different from the previous one
+                doorColors = set(COLOR_NAMES)
+                if prevDoorColor:
+                    doorColors.remove(prevDoorColor)
+                # Note: the use of sorting here guarantees determinism,
+                # This is needed because Python's set is not deterministic
+                doorColor = self._rand_elem(sorted(doorColors))
+
+                entryDoor = Door(doorColor)
+                self.grid.set(room.entryDoorPos[0], room.entryDoorPos[1], entryDoor)
+                prevDoorColor = doorColor
+
+                prevRoom = roomList[idx - 1]
+                prevRoom.exitDoorPos = room.entryDoorPos
+
+        # Randomize the starting agent position and direction
+        self.place_agent(roomList[0].top, roomList[0].size)
+
+        # Place the final goal in the last room
+        self.goal_pos = self.place_obj(Goal(), roomList[-1].top, roomList[-1].size)
+
+        self.mission = "traverse the rooms to get to the goal"
+
+    def _placeRoom(self, numLeft, roomList, minSz, maxSz, entryDoorWall, entryDoorPos):
+        # Choose the room size randomly
+        sizeX = self._rand_int(minSz, maxSz + 1)
+        sizeY = self._rand_int(minSz, maxSz + 1)
+
+        # The first room will be at the door position
+        if len(roomList) == 0:
+            topX, topY = entryDoorPos
+        # Entry on the right
+        elif entryDoorWall == 0:
+            topX = entryDoorPos[0] - sizeX + 1
+            y = entryDoorPos[1]
+            topY = self._rand_int(y - sizeY + 2, y)
+        # Entry wall on the south
+        elif entryDoorWall == 1:
+            x = entryDoorPos[0]
+            topX = self._rand_int(x - sizeX + 2, x)
+            topY = entryDoorPos[1] - sizeY + 1
+        # Entry wall on the left
+        elif entryDoorWall == 2:
+            topX = entryDoorPos[0]
+            y = entryDoorPos[1]
+            topY = self._rand_int(y - sizeY + 2, y)
+        # Entry wall on the top
+        elif entryDoorWall == 3:
+            x = entryDoorPos[0]
+            topX = self._rand_int(x - sizeX + 2, x)
+            topY = entryDoorPos[1]
+        else:
+            assert False, entryDoorWall
+
+        # If the room is out of the grid, can't place a room here
+        if topX < 0 or topY < 0:
+            return False
+        if topX + sizeX > self.width or topY + sizeY >= self.height:
+            return False
+
+        # If the room intersects with previous rooms, can't place it here
+        for room in roomList[:-1]:
+            nonOverlap = (
+                topX + sizeX < room.top[0]
+                or room.top[0] + room.size[0] <= topX
+                or topY + sizeY < room.top[1]
+                or room.top[1] + room.size[1] <= topY
+            )
+
+            if not nonOverlap:
+                return False
+
+        # Add this room to the list
+        roomList.append(MultiRoom((topX, topY), (sizeX, sizeY), entryDoorPos, None))
+
+        # If this was the last room, stop
+        if numLeft == 1:
+            return True
+
+        # Try placing the next room
+        for i in range(0, 8):
+
+            # Pick which wall to place the out door on
+            wallSet = {0, 1, 2, 3}
+            wallSet.remove(entryDoorWall)
+            exitDoorWall = self._rand_elem(sorted(wallSet))
+            nextEntryWall = (exitDoorWall + 2) % 4
+
+            # Pick the exit door position
+            # Exit on right wall
+            if exitDoorWall == 0:
+                exitDoorPos = (topX + sizeX - 1, topY + self._rand_int(1, sizeY - 1))
+            # Exit on south wall
+            elif exitDoorWall == 1:
+                exitDoorPos = (topX + self._rand_int(1, sizeX - 1), topY + sizeY - 1)
+            # Exit on left wall
+            elif exitDoorWall == 2:
+                exitDoorPos = (topX, topY + self._rand_int(1, sizeY - 1))
+            # Exit on north wall
+            elif exitDoorWall == 3:
+                exitDoorPos = (topX + self._rand_int(1, sizeX - 1), topY)
+            else:
+                assert False
+
+            # Recursively create the other rooms
+            success = self._placeRoom(
+                numLeft - 1,
+                roomList=roomList,
+                minSz=minSz,
+                maxSz=maxSz,
+                entryDoorWall=nextEntryWall,
+                entryDoorPos=exitDoorPos,
+            )
+
+            if success:
+                break
+
+        return True

gym_minigrid/gym_minigrid/envs/obstructedmaze.py

@@ -0,0 +1,246 @@
+from gym_minigrid.minigrid import COLOR_NAMES, DIR_TO_VEC, Ball, Box, Key, MissionSpace
+from gym_minigrid.roomgrid import RoomGrid
+
+
+class ObstructedMazeEnv(RoomGrid):
+
+    """
+    ### Description
+
+    The agent has to pick up a box which is placed in a corner of a 3x3 maze.
+    The doors are locked, the keys are hidden in boxes and doors are obstructed
+    by balls. This environment can be solved without relying on language.
+
+    ### Mission Space
+
+    "pick up the {COLOR_NAMES[0]} ball"
+
+    ### Action Space
+
+    | Num | Name         | Action                    |
+    |-----|--------------|---------------------------|
+    | 0   | left         | Turn left                 |
+    | 1   | right        | Turn right                |
+    | 2   | forward      | Move forward              |
+    | 3   | pickup       | Pick up an object         |
+    | 4   | drop         | Unused                    |
+    | 5   | toggle       | Toggle/activate an object |
+    | 6   | done         | Unused                    |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent picks up the blue ball.
+    2. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    "NDl" are the number of doors locked.
+    "h" if the key is hidden in a box.
+    "b" if the door is obstructed by a ball.
+    "Q" number of quarters that will have doors and keys out of the 9 that the
+    map already has.
+    "Full" 3x3 maze with "h" and "b" options.
+
+    - `MiniGrid-ObstructedMaze-1Dl-v0`
+    - `MiniGrid-ObstructedMaze-1Dlh-v0`
+    - `MiniGrid-ObstructedMaze-1Dlhb-v0`
+    - `MiniGrid-ObstructedMaze-2Dl-v0`
+    - `MiniGrid-ObstructedMaze-2Dlh-v0`
+    - `MiniGrid-ObstructedMaze-2Dlhb-v0`
+    - `MiniGrid-ObstructedMaze-1Q-v0`
+    - `MiniGrid-ObstructedMaze-2Q-v0`
+    - `MiniGrid-ObstructedMaze-Full-v0`
+
+    """
+
+    def __init__(self, num_rows, num_cols, num_rooms_visited, **kwargs):
+        room_size = 6
+        max_steps = 4 * num_rooms_visited * room_size**2
+
+        mission_space = MissionSpace(
+            mission_func=lambda: f"pick up the {COLOR_NAMES[0]} ball",
+        )
+        super().__init__(
+            mission_space=mission_space,
+            room_size=room_size,
+            num_rows=num_rows,
+            num_cols=num_cols,
+            max_steps=max_steps,
+            **kwargs,
+        )
+        self.obj = Ball()  # initialize the obj attribute, that will be changed later on
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        # Define all possible colors for doors
+        self.door_colors = self._rand_subset(COLOR_NAMES, len(COLOR_NAMES))
+        # Define the color of the ball to pick up
+        self.ball_to_find_color = COLOR_NAMES[0]
+        # Define the color of the balls that obstruct doors
+        self.blocking_ball_color = COLOR_NAMES[1]
+        # Define the color of boxes in which keys are hidden
+        self.box_color = COLOR_NAMES[2]
+
+        self.mission = "pick up the %s ball" % self.ball_to_find_color
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        if action == self.actions.pickup:
+            if self.carrying and self.carrying == self.obj:
+                reward = self._reward()
+                terminated = True
+
+        return obs, reward, terminated, truncated, info
+
+    def add_door(
+        self,
+        i,
+        j,
+        door_idx=0,
+        color=None,
+        locked=False,
+        key_in_box=False,
+        blocked=False,
+    ):
+        """
+        Add a door. If the door must be locked, it also adds the key.
+        If the key must be hidden, it is put in a box. If the door must
+        be obstructed, it adds a ball in front of the door.
+        """
+
+        door, door_pos = super().add_door(i, j, door_idx, color, locked=locked)
+
+        if blocked:
+            vec = DIR_TO_VEC[door_idx]
+            blocking_ball = Ball(self.blocking_ball_color) if blocked else None
+            self.grid.set(door_pos[0] - vec[0], door_pos[1] - vec[1], blocking_ball)
+
+        if locked:
+            obj = Key(door.color)
+            if key_in_box:
+                box = Box(self.box_color)
+                box.contains = obj
+                obj = box
+            self.place_in_room(i, j, obj)
+
+        return door, door_pos
+
+
+class ObstructedMaze_1Dlhb(ObstructedMazeEnv):
+    """
+    A blue ball is hidden in a 2x1 maze. A locked door separates
+    rooms. Doors are obstructed by a ball and keys are hidden in boxes.
+    """
+
+    def __init__(self, key_in_box=True, blocked=True, **kwargs):
+        self.key_in_box = key_in_box
+        self.blocked = blocked
+
+        super().__init__(num_rows=1, num_cols=2, num_rooms_visited=2, **kwargs)
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        self.add_door(
+            0,
+            0,
+            door_idx=0,
+            color=self.door_colors[0],
+            locked=True,
+            key_in_box=self.key_in_box,
+            blocked=self.blocked,
+        )
+
+        self.obj, _ = self.add_object(1, 0, "ball", color=self.ball_to_find_color)
+        self.place_agent(0, 0)
+
+
+class ObstructedMaze_Full(ObstructedMazeEnv):
+    """
+    A blue ball is hidden in one of the 4 corners of a 3x3 maze. Doors
+    are locked, doors are obstructed by a ball and keys are hidden in
+    boxes.
+    """
+
+    def __init__(
+        self,
+        agent_room=(1, 1),
+        key_in_box=True,
+        blocked=True,
+        num_quarters=4,
+        num_rooms_visited=25,
+        **kwargs,
+    ):
+        self.agent_room = agent_room
+        self.key_in_box = key_in_box
+        self.blocked = blocked
+        self.num_quarters = num_quarters
+
+        super().__init__(
+            num_rows=3, num_cols=3, num_rooms_visited=num_rooms_visited, **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        middle_room = (1, 1)
+        # Define positions of "side rooms" i.e. rooms that are neither
+        # corners nor the center.
+        side_rooms = [(2, 1), (1, 2), (0, 1), (1, 0)][: self.num_quarters]
+        for i in range(len(side_rooms)):
+            side_room = side_rooms[i]
+
+            # Add a door between the center room and the side room
+            self.add_door(
+                *middle_room, door_idx=i, color=self.door_colors[i], locked=False
+            )
+
+            for k in [-1, 1]:
+                # Add a door to each side of the side room
+                self.add_door(
+                    *side_room,
+                    locked=True,
+                    door_idx=(i + k) % 4,
+                    color=self.door_colors[(i + k) % len(self.door_colors)],
+                    key_in_box=self.key_in_box,
+                    blocked=self.blocked,
+                )
+
+        corners = [(2, 0), (2, 2), (0, 2), (0, 0)][: self.num_quarters]
+        ball_room = self._rand_elem(corners)
+
+        self.obj, _ = self.add_object(
+            ball_room[0], ball_room[1], "ball", color=self.ball_to_find_color
+        )
+        self.place_agent(*self.agent_room)
+
+
+class ObstructedMaze_2Dl(ObstructedMaze_Full):
+    def __init__(self, **kwargs):
+        super().__init__((2, 1), False, False, 1, 4, **kwargs)
+
+
+class ObstructedMaze_2Dlh(ObstructedMaze_Full):
+    def __init__(self, **kwargs):
+        super().__init__((2, 1), True, False, 1, 4, **kwargs)
+
+
+class ObstructedMaze_2Dlhb(ObstructedMaze_Full):
+    def __init__(self, **kwargs):
+        super().__init__((2, 1), True, True, 1, 4, **kwargs)

gym_minigrid/gym_minigrid/envs/playground.py

@@ -0,0 +1,90 @@
+from gym_minigrid.minigrid import (
+    COLOR_NAMES,
+    Ball,
+    Box,
+    Door,
+    Grid,
+    Key,
+    MiniGridEnv,
+    MissionSpace,
+)
+
+
+class PlaygroundEnv(MiniGridEnv):
+    """
+    Environment with multiple rooms and random objects.
+    This environment has no specific goals or rewards.
+    """
+
+    def __init__(self, **kwargs):
+        mission_space = MissionSpace(mission_func=lambda: "")
+        self.size = 19
+        super().__init__(
+            mission_space=mission_space,
+            width=self.size,
+            height=self.size,
+            max_steps=100,
+            **kwargs
+        )
+
+    def _gen_grid(self, width, height):
+        # Create the grid
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.horz_wall(0, 0)
+        self.grid.horz_wall(0, height - 1)
+        self.grid.vert_wall(0, 0)
+        self.grid.vert_wall(width - 1, 0)
+
+        roomW = width // 3
+        roomH = height // 3
+
+        # For each row of rooms
+        for j in range(0, 3):
+
+            # For each column
+            for i in range(0, 3):
+                xL = i * roomW
+                yT = j * roomH
+                xR = xL + roomW
+                yB = yT + roomH
+
+                # Bottom wall and door
+                if i + 1 < 3:
+                    self.grid.vert_wall(xR, yT, roomH)
+                    pos = (xR, self._rand_int(yT + 1, yB - 1))
+                    color = self._rand_elem(COLOR_NAMES)
+                    self.grid.set(*pos, Door(color))
+
+                # Bottom wall and door
+                if j + 1 < 3:
+                    self.grid.horz_wall(xL, yB, roomW)
+                    pos = (self._rand_int(xL + 1, xR - 1), yB)
+                    color = self._rand_elem(COLOR_NAMES)
+                    self.grid.set(*pos, Door(color))
+
+        # Randomize the player start position and orientation
+        self.place_agent()
+
+        # Place random objects in the world
+        types = ["key", "ball", "box"]
+        for i in range(0, 12):
+            objType = self._rand_elem(types)
+            objColor = self._rand_elem(COLOR_NAMES)
+            if objType == "key":
+                obj = Key(objColor)
+            elif objType == "ball":
+                obj = Ball(objColor)
+            elif objType == "box":
+                obj = Box(objColor)
+            else:
+                raise ValueError(
+                    "{} object type given. Object type can only be of values key, ball and box.".format(
+                        objType
+                    )
+                )
+            self.place_obj(obj)
+
+        # No explicit mission in this environment
+        self.mission = ""

gym_minigrid/gym_minigrid/envs/putnear.py

@@ -0,0 +1,192 @@
+from gym_minigrid.minigrid import (
+    COLOR_NAMES,
+    Ball,
+    Box,
+    Grid,
+    Key,
+    MiniGridEnv,
+    MissionSpace,
+)
+
+
+class PutNearEnv(MiniGridEnv):
+
+    """
+    ### Description
+
+    The agent is instructed through a textual string to pick up an object and
+    place it next to another object. This environment is easy to solve with two
+    objects, but difficult to solve with more, as it involves both textual
+    understanding and spatial reasoning involving multiple objects.
+
+    ### Mission Space
+
+    "put the {move_color} {move_type} near the {target_color} {target_type}"
+
+    {move_color} and {target_color} can be "red", "green", "blue", "purple",
+    "yellow" or "grey".
+
+    {move_type} and {target_type} Can be "box", "ball" or "key".
+
+    ### Action Space
+
+    | Num | Name         | Action            |
+    |-----|--------------|-------------------|
+    | 0   | left         | Turn left         |
+    | 1   | right        | Turn right        |
+    | 2   | forward      | Move forward      |
+    | 3   | pickup       | Pick up an object |
+    | 4   | drop         | Drop an object    |
+    | 5   | toggle       | Unused            |
+    | 6   | done         | Unused            |
+
+    ### Observation Encoding
+
+    - Each tile is encoded as a 3 dimensional tuple:
+        `(OBJECT_IDX, COLOR_IDX, STATE)`
+    - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
+        [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
+    - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
+
+    ### Rewards
+
+    A reward of '1' is given for success, and '0' for failure.
+
+    ### Termination
+
+    The episode ends if any one of the following conditions is met:
+
+    1. The agent picks up the wrong object.
+    2. The agent drop the correct object near the target.
+    3. Timeout (see `max_steps`).
+
+    ### Registered Configurations
+
+    N: number of objects.
+
+    - `MiniGrid-PutNear-6x6-N2-v0`
+    - `MiniGrid-PutNear-8x8-N3-v0`
+
+    """
+
+    def __init__(self, size=6, numObjs=2, **kwargs):
+        self.size = size
+        self.numObjs = numObjs
+        self.obj_types = ["key", "ball", "box"]
+        mission_space = MissionSpace(
+            mission_func=lambda move_color, move_type, target_color, target_type: f"put the {move_color} {move_type} near the {target_color} {target_type}",
+            ordered_placeholders=[
+                COLOR_NAMES,
+                self.obj_types,
+                COLOR_NAMES,
+                self.obj_types,
+            ],
+        )
+        super().__init__(
+            mission_space=mission_space,
+            width=size,
+            height=size,
+            max_steps=5 * size,
+            # Set this to True for maximum speed
+            see_through_walls=True,
+            **kwargs,
+        )
+
+    def _gen_grid(self, width, height):
+        self.grid = Grid(width, height)
+
+        # Generate the surrounding walls
+        self.grid.horz_wall(0, 0)
+        self.grid.horz_wall(0, height - 1)
+        self.grid.vert_wall(0, 0)
+        self.grid.vert_wall(width - 1, 0)
+
+        # Types and colors of objects we can generate
+        types = ["key", "ball", "box"]
+
+        objs = []
+        objPos = []
+
+        def near_obj(env, p1):
+            for p2 in objPos:
+                dx = p1[0] - p2[0]
+                dy = p1[1] - p2[1]
+                if abs(dx) <= 1 and abs(dy) <= 1:
+                    return True
+            return False
+
+        # Until we have generated all the objects
+        while len(objs) < self.numObjs:
+            objType = self._rand_elem(types)
+            objColor = self._rand_elem(COLOR_NAMES)
+
+            # If this object already exists, try again
+            if (objType, objColor) in objs:
+                continue
+
+            if objType == "key":
+                obj = Key(objColor)
+            elif objType == "ball":
+                obj = Ball(objColor)
+            elif objType == "box":
+                obj = Box(objColor)
+            else:
+                raise ValueError(
+                    "{} object type given. Object type can only be of values key, ball and box.".format(
+                        objType
+                    )
+                )
+
+            pos = self.place_obj(obj, reject_fn=near_obj)
+
+            objs.append((objType, objColor))
+            objPos.append(pos)
+
+        # Randomize the agent start position and orientation
+        self.place_agent()
+
+        # Choose a random object to be moved
+        objIdx = self._rand_int(0, len(objs))
+        self.move_type, self.moveColor = objs[objIdx]
+        self.move_pos = objPos[objIdx]
+
+        # Choose a target object (to put the first object next to)
+        while True:
+            targetIdx = self._rand_int(0, len(objs))
+            if targetIdx != objIdx:
+                break
+        self.target_type, self.target_color = objs[targetIdx]
+        self.target_pos = objPos[targetIdx]
+
+        self.mission = "put the {} {} near the {} {}".format(
+            self.moveColor,
+            self.move_type,
+            self.target_color,
+            self.target_type,
+        )
+
+    def step(self, action):
+        preCarrying = self.carrying
+
+        obs, reward, terminated, truncated, info = super().step(action)
+
+        u, v = self.dir_vec
+        ox, oy = (self.agent_pos[0] + u, self.agent_pos[1] + v)
+        tx, ty = self.target_pos
+
+        # If we picked up the wrong object, terminate the episode
+        if action == self.actions.pickup and self.carrying:
+            if (
+                self.carrying.type != self.move_type
+                or self.carrying.color != self.moveColor
+            ):
+                terminated = True
+
+        # If successfully dropping an object near the target
+        if action == self.actions.drop and preCarrying:
+            if self.grid.get(ox, oy) is preCarrying:
+                if abs(ox - tx) <= 1 and abs(oy - ty) <= 1:
+                    reward = self._reward()
+            terminated = True
+
+        return obs, reward, terminated, truncated, info