Thomas Knoll
1 year ago
3 changed files with 587 additions and 26 deletions
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25examples/shields/rl/dqn_rllib.ipynb
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30examples/shields/rl/ppo_rllib.ipynb
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552examples/shields/rl/tutorial.ipynb
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"The requisites for applying a shield while training a RL Agent in the Minigrid Environment with PPO Algorithm are:\n", |
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"\n", |
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"# Binaries\n", |
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"- Tempest\n", |
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"- Minigrid2Prism\n", |
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"\n", |
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"\n", |
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"# Python packages:\n", |
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"- Tempestpy\n", |
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"- Minigrid with the printGrid Function\n", |
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"- ray / rllib" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"The shield handler is responsible for creating and querying the shield." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"import stormpy\n", |
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"import stormpy.core\n", |
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"import stormpy.simulator\n", |
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"\n", |
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"import stormpy.shields\n", |
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"import stormpy.logic\n", |
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"\n", |
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"import stormpy.examples\n", |
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"import stormpy.examples.files\n", |
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"\n", |
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"from abc import ABC\n", |
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"\n", |
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"import os\n", |
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"\n", |
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"class ShieldHandler(ABC):\n", |
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" def __init__(self) -> None:\n", |
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" pass\n", |
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" def create_shield(self, **kwargs) -> dict:\n", |
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" pass\n", |
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"\n", |
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"class MiniGridShieldHandler(ShieldHandler):\n", |
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" def __init__(self, grid_file, grid_to_prism_path, prism_path, formula) -> None:\n", |
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" self.grid_file = grid_file\n", |
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" self.grid_to_prism_path = grid_to_prism_path\n", |
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" self.prism_path = prism_path\n", |
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" self.formula = formula\n", |
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" \n", |
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" def __export_grid_to_text(self, env):\n", |
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" f = open(self.grid_file, \"w\")\n", |
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" f.write(env.printGrid(init=True))\n", |
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" f.close()\n", |
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"\n", |
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" \n", |
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" def __create_prism(self):\n", |
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" result = os.system(F\"{self.grid_to_prism_path} -v 'agent' -i {self.grid_file} -o {self.prism_path}\")\n", |
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" \n", |
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" assert result == 0, \"Prism file could not be generated\"\n", |
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" \n", |
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" f = open(self.prism_path, \"a\")\n", |
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" f.write(\"label \\\"AgentIsInLava\\\" = AgentIsInLava;\")\n", |
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" f.close()\n", |
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" \n", |
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" def __create_shield_dict(self):\n", |
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" program = stormpy.parse_prism_program(self.prism_path)\n", |
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" shield_specification = stormpy.logic.ShieldExpression(stormpy.logic.ShieldingType.PRE_SAFETY, stormpy.logic.ShieldComparison.RELATIVE, 0.1) \n", |
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" \n", |
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" formulas = stormpy.parse_properties_for_prism_program(self.formula, program)\n", |
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" options = stormpy.BuilderOptions([p.raw_formula for p in formulas])\n", |
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" options.set_build_state_valuations(True)\n", |
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" options.set_build_choice_labels(True)\n", |
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" options.set_build_all_labels()\n", |
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" model = stormpy.build_sparse_model_with_options(program, options)\n", |
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" \n", |
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" result = stormpy.model_checking(model, formulas[0], extract_scheduler=True, shield_expression=shield_specification)\n", |
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" \n", |
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" assert result.has_scheduler\n", |
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" assert result.has_shield\n", |
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" shield = result.shield\n", |
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" \n", |
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" action_dictionary = {}\n", |
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" shield_scheduler = shield.construct()\n", |
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" \n", |
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" for stateID in model.states:\n", |
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" choice = shield_scheduler.get_choice(stateID)\n", |
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" choices = choice.choice_map\n", |
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" state_valuation = model.state_valuations.get_string(stateID)\n", |
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"\n", |
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" actions_to_be_executed = [(choice[1] ,model.choice_labeling.get_labels_of_choice(model.get_choice_index(stateID, choice[1]))) for choice in choices]\n", |
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"\n", |
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" action_dictionary[state_valuation] = actions_to_be_executed\n", |
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"\n", |
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" stormpy.shields.export_shield(model, shield, \"Grid.shield\")\n", |
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" \n", |
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" return action_dictionary\n", |
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" \n", |
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" \n", |
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" def create_shield(self, **kwargs):\n", |
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" env = kwargs[\"env\"]\n", |
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" self.__export_grid_to_text(env)\n", |
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" self.__create_prism()\n", |
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" \n", |
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" return self.__create_shield_dict()\n", |
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" \n", |
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"def create_shield_query(env):\n", |
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" coordinates = env.env.agent_pos\n", |
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" view_direction = env.env.agent_dir\n", |
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"\n", |
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" key_text = \"\"\n", |
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"\n", |
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" # only support one key for now\n", |
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" \n", |
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" #print(F\"Agent pos is {self.env.agent_pos} and direction {self.env.agent_dir} \")\n", |
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" cur_pos_str = f\"[{key_text}!AgentDone\\t& xAgent={coordinates[0]}\\t& yAgent={coordinates[1]}\\t& viewAgent={view_direction}]\"\n", |
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"\n", |
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" return cur_pos_str\n", |
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" " |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"To train a learning algorithm with shielding the allowed actions need to be embedded in the observation. \n", |
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"This can be done by implementing a gym wrapper handling the action embedding for the enviornment." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import gymnasium as gym\n", |
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"import numpy as np\n", |
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"\n", |
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"from minigrid.core.actions import Actions\n", |
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"\n", |
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"from gymnasium.spaces import Dict, Box\n", |
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"from collections import deque\n", |
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"from ray.rllib.utils.numpy import one_hot\n", |
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"\n", |
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"from helpers import get_action_index_mapping, extract_keys\n", |
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"\n", |
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"class OneHotShieldingWrapper(gym.core.ObservationWrapper):\n", |
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" def __init__(self, env, vector_index, framestack):\n", |
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" super().__init__(env)\n", |
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" self.framestack = framestack\n", |
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" # 49=7x7 field of vision; 11=object types; 6=colors; 3=state types.\n", |
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" # +4: Direction.\n", |
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" self.single_frame_dim = 49 * (11 + 6 + 3) + 4\n", |
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" self.init_x = None\n", |
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" self.init_y = None\n", |
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" self.x_positions = []\n", |
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" self.y_positions = []\n", |
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" self.x_y_delta_buffer = deque(maxlen=100)\n", |
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" self.vector_index = vector_index\n", |
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" self.frame_buffer = deque(maxlen=self.framestack)\n", |
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" for _ in range(self.framestack):\n", |
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" self.frame_buffer.append(np.zeros((self.single_frame_dim,)))\n", |
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"\n", |
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" self.observation_space = Dict(\n", |
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" {\n", |
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" \"data\": gym.spaces.Box(0.0, 1.0, shape=(self.single_frame_dim * self.framestack,), dtype=np.float32),\n", |
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" \"action_mask\": gym.spaces.Box(0, 10, shape=(env.action_space.n,), dtype=int),\n", |
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" }\n", |
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" )\n", |
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"\n", |
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" def observation(self, obs):\n", |
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" # Debug output: max-x/y positions to watch exploration progress.\n", |
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" # print(F\"Initial observation in Wrapper {obs}\")\n", |
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" if self.step_count == 0:\n", |
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" for _ in range(self.framestack):\n", |
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" self.frame_buffer.append(np.zeros((self.single_frame_dim,)))\n", |
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" if self.vector_index == 0:\n", |
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" if self.x_positions:\n", |
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" max_diff = max(\n", |
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" np.sqrt(\n", |
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" (np.array(self.x_positions) - self.init_x) ** 2\n", |
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" + (np.array(self.y_positions) - self.init_y) ** 2\n", |
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" )\n", |
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" )\n", |
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" self.x_y_delta_buffer.append(max_diff)\n", |
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" print(\n", |
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" \"100-average dist travelled={}\".format(\n", |
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" np.mean(self.x_y_delta_buffer)\n", |
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" )\n", |
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" )\n", |
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" self.x_positions = []\n", |
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" self.y_positions = []\n", |
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" self.init_x = self.agent_pos[0]\n", |
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" self.init_y = self.agent_pos[1]\n", |
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"\n", |
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"\n", |
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" self.x_positions.append(self.agent_pos[0])\n", |
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" self.y_positions.append(self.agent_pos[1])\n", |
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"\n", |
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" image = obs[\"data\"]\n", |
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"\n", |
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" # One-hot the last dim into 11, 6, 3 one-hot vectors, then flatten.\n", |
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" objects = one_hot(image[:, :, 0], depth=11)\n", |
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" colors = one_hot(image[:, :, 1], depth=6)\n", |
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" states = one_hot(image[:, :, 2], depth=3)\n", |
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"\n", |
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" all_ = np.concatenate([objects, colors, states], -1)\n", |
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" all_flat = np.reshape(all_, (-1,))\n", |
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" direction = one_hot(np.array(self.agent_dir), depth=4).astype(np.float32)\n", |
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" single_frame = np.concatenate([all_flat, direction])\n", |
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" self.frame_buffer.append(single_frame)\n", |
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"\n", |
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" tmp = {\"data\": np.concatenate(self.frame_buffer), \"action_mask\": obs[\"action_mask\"] }\n", |
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" return tmp\n", |
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"\n", |
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"# Environment wrapper handling action embedding in observations\n", |
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"class MiniGridShieldingWrapper(gym.core.Wrapper):\n", |
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" def __init__(self, \n", |
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" env, \n", |
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" shield_creator : ShieldHandler,\n", |
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" shield_query_creator,\n", |
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" create_shield_at_reset=True, \n", |
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" mask_actions=True):\n", |
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" super(MiniGridShieldingWrapper, self).__init__(env)\n", |
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" self.max_available_actions = env.action_space.n\n", |
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" self.observation_space = Dict(\n", |
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" {\n", |
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" \"data\": env.observation_space.spaces[\"image\"],\n", |
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" \"action_mask\" : Box(0, 10, shape=(self.max_available_actions,), dtype=np.int8),\n", |
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" }\n", |
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" )\n", |
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" self.shield_creator = shield_creator\n", |
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" self.create_shield_at_reset = create_shield_at_reset\n", |
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" self.shield = shield_creator.create_shield(env=self.env)\n", |
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" self.mask_actions = mask_actions\n", |
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" self.shield_query_creator = shield_query_creator\n", |
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"\n", |
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" def create_action_mask(self):\n", |
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" if not self.mask_actions:\n", |
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" return np.array([1.0] * self.max_available_actions, dtype=np.int8)\n", |
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" \n", |
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" cur_pos_str = self.shield_query_creator(self.env)\n", |
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" \n", |
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" # Create the mask\n", |
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" # If shield restricts action mask only valid with 1.0\n", |
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" # else set all actions as valid\n", |
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" allowed_actions = []\n", |
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" mask = np.array([0.0] * self.max_available_actions, dtype=np.int8)\n", |
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"\n", |
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" if cur_pos_str in self.shield and self.shield[cur_pos_str]:\n", |
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" allowed_actions = self.shield[cur_pos_str]\n", |
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" for allowed_action in allowed_actions:\n", |
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" index = get_action_index_mapping(allowed_action[1]) # Allowed_action is a set\n", |
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" if index is None:\n", |
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" assert(False)\n", |
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" mask[index] = 1.0\n", |
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" else:\n", |
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" for index, x in enumerate(mask):\n", |
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" mask[index] = 1.0\n", |
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" \n", |
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" front_tile = self.env.grid.get(self.env.front_pos[0], self.env.front_pos[1])\n", |
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"\n", |
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" if front_tile is not None and front_tile.type == \"key\":\n", |
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" mask[Actions.pickup] = 1.0\n", |
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" \n", |
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" if front_tile and front_tile.type == \"door\":\n", |
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" mask[Actions.toggle] = 1.0\n", |
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" \n", |
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" return mask\n", |
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"\n", |
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" def reset(self, *, seed=None, options=None):\n", |
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" obs, infos = self.env.reset(seed=seed, options=options)\n", |
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" \n", |
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" if self.create_shield_at_reset and self.mask_actions:\n", |
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" self.shield = self.shield_creator.create_shield(env=self.env)\n", |
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" \n", |
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" self.keys = extract_keys(self.env)\n", |
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" mask = self.create_action_mask()\n", |
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" return {\n", |
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" \"data\": obs[\"image\"],\n", |
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" \"action_mask\": mask\n", |
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" }, infos\n", |
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"\n", |
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" def step(self, action):\n", |
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" orig_obs, rew, done, truncated, info = self.env.step(action)\n", |
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"\n", |
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" mask = self.create_action_mask()\n", |
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" obs = {\n", |
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" \"data\": orig_obs[\"image\"],\n", |
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" \"action_mask\": mask,\n", |
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" }\n", |
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" \n", |
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" return obs, rew, done, truncated, info\n", |
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"\n", |
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"\n", |
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"# Wrapper to use with a stable baseline algorithm\n", |
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"class MiniGridSbShieldingWrapper(gym.core.Wrapper):\n", |
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" def __init__(self, \n", |
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" env, \n", |
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" shield_creator : ShieldHandler,\n", |
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" shield_query_creator,\n", |
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" create_shield_at_reset = True,\n", |
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" mask_actions=True,\n", |
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" ):\n", |
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" super(MiniGridSbShieldingWrapper, self).__init__(env)\n", |
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" self.max_available_actions = env.action_space.n\n", |
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" self.observation_space = env.observation_space.spaces[\"image\"]\n", |
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" \n", |
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" self.shield_creator = shield_creator\n", |
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" self.mask_actions = mask_actions\n", |
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" self.shield_query_creator = shield_query_creator\n", |
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"\n", |
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" def create_action_mask(self):\n", |
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" if not self.mask_actions:\n", |
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" return np.array([1.0] * self.max_available_actions, dtype=np.int8)\n", |
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" \n", |
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" cur_pos_str = self.shield_query_creator(self.env)\n", |
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" \n", |
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" allowed_actions = []\n", |
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"\n", |
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" # Create the mask\n", |
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" # If shield restricts actions, mask only valid actions with 1.0\n", |
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" # else set all actions valid\n", |
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" mask = np.array([0.0] * self.max_available_actions, dtype=np.int8)\n", |
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"\n", |
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" if cur_pos_str in self.shield and self.shield[cur_pos_str]:\n", |
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" allowed_actions = self.shield[cur_pos_str]\n", |
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" for allowed_action in allowed_actions:\n", |
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" index = get_action_index_mapping(allowed_action[1])\n", |
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" if index is None:\n", |
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" assert(False)\n", |
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" \n", |
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" mask[index] = 1.0\n", |
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" else:\n", |
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" for index, x in enumerate(mask):\n", |
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" mask[index] = 1.0\n", |
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" \n", |
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" front_tile = self.env.grid.get(self.env.front_pos[0], self.env.front_pos[1])\n", |
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"\n", |
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" \n", |
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" if front_tile and front_tile.type == \"door\":\n", |
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" mask[Actions.toggle] = 1.0 \n", |
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" \n", |
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" return mask \n", |
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" \n", |
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"\n", |
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" def reset(self, *, seed=None, options=None):\n", |
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" obs, infos = self.env.reset(seed=seed, options=options)\n", |
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" \n", |
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" keys = extract_keys(self.env)\n", |
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" shield = self.shield_creator.create_shield(env=self.env)\n", |
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" \n", |
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" self.keys = keys\n", |
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" self.shield = shield\n", |
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" return obs[\"image\"], infos\n", |
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"\n", |
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" def step(self, action):\n", |
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" orig_obs, rew, done, truncated, info = self.env.step(action)\n", |
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" obs = orig_obs[\"image\"]\n", |
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" \n", |
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" return obs, rew, done, truncated, info\n", |
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"\n" |
||||
|
] |
||||
|
}, |
||||
|
{ |
||||
|
"cell_type": "markdown", |
||||
|
"metadata": {}, |
||||
|
"source": [ |
||||
|
"If we want to use rllib algorithms we additionaly need a model which performs the action masking." |
||||
|
] |
||||
|
}, |
||||
|
{ |
||||
|
"cell_type": "code", |
||||
|
"execution_count": null, |
||||
|
"metadata": {}, |
||||
|
"outputs": [], |
||||
|
"source": [ |
||||
|
"from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC\n", |
||||
|
"from ray.rllib.models.torch.torch_modelv2 import TorchModelV2\n", |
||||
|
"from ray.rllib.utils.framework import try_import_torch\n", |
||||
|
"from ray.rllib.utils.torch_utils import FLOAT_MIN, FLOAT_MAX\n", |
||||
|
"\n", |
||||
|
"torch, nn = try_import_torch()\n", |
||||
|
"\n", |
||||
|
"class TorchActionMaskModel(TorchModelV2, nn.Module):\n", |
||||
|
"\n", |
||||
|
" def __init__(\n", |
||||
|
" self,\n", |
||||
|
" obs_space,\n", |
||||
|
" action_space,\n", |
||||
|
" num_outputs,\n", |
||||
|
" model_config,\n", |
||||
|
" name,\n", |
||||
|
" **kwargs,\n", |
||||
|
" ):\n", |
||||
|
" orig_space = getattr(obs_space, \"original_space\", obs_space)\n", |
||||
|
" \n", |
||||
|
" TorchModelV2.__init__(\n", |
||||
|
" self, obs_space, action_space, num_outputs, model_config, name, **kwargs\n", |
||||
|
" )\n", |
||||
|
" nn.Module.__init__(self)\n", |
||||
|
" \n", |
||||
|
" self.count = 0\n", |
||||
|
"\n", |
||||
|
" self.internal_model = TorchFC(\n", |
||||
|
" orig_space[\"data\"],\n", |
||||
|
" action_space,\n", |
||||
|
" num_outputs,\n", |
||||
|
" model_config,\n", |
||||
|
" name + \"_internal\",\n", |
||||
|
" )\n", |
||||
|
" \n", |
||||
|
"\n", |
||||
|
" def forward(self, input_dict, state, seq_lens):\n", |
||||
|
" # Extract the available actions tensor from the observation.\n", |
||||
|
" # Compute the unmasked logits.\n", |
||||
|
" logits, _ = self.internal_model({\"obs\": input_dict[\"obs\"][\"data\"]})\n", |
||||
|
" \n", |
||||
|
" action_mask = input_dict[\"obs\"][\"action_mask\"]\n", |
||||
|
"\n", |
||||
|
" inf_mask = torch.clamp(torch.log(action_mask), min=FLOAT_MIN)\n", |
||||
|
" masked_logits = logits + inf_mask\n", |
||||
|
"\n", |
||||
|
" # Return masked logits.\n", |
||||
|
" return masked_logits, state\n", |
||||
|
"\n", |
||||
|
" def value_function(self):\n", |
||||
|
" return self.internal_model.value_function()" |
||||
|
] |
||||
|
}, |
||||
|
{ |
||||
|
"cell_type": "markdown", |
||||
|
"metadata": {}, |
||||
|
"source": [ |
||||
|
"Using these components we can now train an rl agent with shielding." |
||||
|
] |
||||
|
}, |
||||
|
{ |
||||
|
"cell_type": "code", |
||||
|
"execution_count": null, |
||||
|
"metadata": {}, |
||||
|
"outputs": [], |
||||
|
"source": [ |
||||
|
"import gymnasium as gym\n", |
||||
|
"import minigrid\n", |
||||
|
"\n", |
||||
|
"from ray import tune, air\n", |
||||
|
"from ray.tune import register_env\n", |
||||
|
"from ray.rllib.algorithms.ppo import PPOConfig\n", |
||||
|
"from ray.tune.logger import pretty_print\n", |
||||
|
"from ray.rllib.models import ModelCatalog\n", |
||||
|
"\n", |
||||
|
"\n", |
||||
|
"def shielding_env_creater(config):\n", |
||||
|
" name = config.get(\"name\", \"MiniGrid-LavaCrossingS9N1-v0\")\n", |
||||
|
" framestack = config.get(\"framestack\", 4)\n", |
||||
|
" \n", |
||||
|
" shield_creator = MiniGridShieldHandler(\"grid.txt\", \"./main\", \"grid.prism\", \"Pmax=? [G !\\\"AgentIsInLavaAndNotDone\\\"]\")\n", |
||||
|
" \n", |
||||
|
" env = gym.make(name)\n", |
||||
|
" env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query ,mask_actions=True)\n", |
||||
|
" env = OneHotShieldingWrapper(env, config.vector_index if hasattr(config, \"vector_index\") else 0,\n", |
||||
|
" framestack=framestack)\n", |
||||
|
" \n", |
||||
|
" return env\n", |
||||
|
"\n", |
||||
|
"\n", |
||||
|
"def register_minigrid_shielding_env():\n", |
||||
|
" env_name = \"mini-grid-shielding\"\n", |
||||
|
" register_env(env_name, shielding_env_creater)\n", |
||||
|
" ModelCatalog.register_custom_model(\n", |
||||
|
" \"shielding_model\", \n", |
||||
|
" TorchActionMaskModel)\n", |
||||
|
"\n", |
||||
|
"register_minigrid_shielding_env()\n", |
||||
|
"\n", |
||||
|
"\n", |
||||
|
"config = (PPOConfig()\n", |
||||
|
" .rollouts(num_rollout_workers=1)\n", |
||||
|
" .resources(num_gpus=0)\n", |
||||
|
" .environment(env=\"mini-grid-shielding\", env_config={\"name\": \"MiniGrid-LavaCrossingS9N1-v0\"})\n", |
||||
|
" .framework(\"torch\")\n", |
||||
|
" .rl_module(_enable_rl_module_api = False)\n", |
||||
|
" .training(_enable_learner_api=False ,model={\n", |
||||
|
" \"custom_model\": \"shielding_model\"\n", |
||||
|
" }))\n", |
||||
|
"\n", |
||||
|
"tuner = tune.Tuner(\"PPO\",\n", |
||||
|
" tune_config=tune.TuneConfig(\n", |
||||
|
" metric=\"episode_reward_mean\",\n", |
||||
|
" mode=\"max\",\n", |
||||
|
" num_samples=1,\n", |
||||
|
" \n", |
||||
|
" ),\n", |
||||
|
" run_config=air.RunConfig(\n", |
||||
|
" stop = {\"episode_reward_mean\": 94,\n", |
||||
|
" \"timesteps_total\": 12000,\n", |
||||
|
" \"training_iteration\": 12}, \n", |
||||
|
" checkpoint_config=air.CheckpointConfig(checkpoint_at_end=True, num_to_keep=2 ),\n", |
||||
|
" ),\n", |
||||
|
" param_space=config,)\n", |
||||
|
"\n", |
||||
|
"results = tuner.fit()\n", |
||||
|
"best_result = results.get_best_result()\n", |
||||
|
"\n", |
||||
|
"import pprint\n", |
||||
|
"\n", |
||||
|
"metrics_to_print = [\n", |
||||
|
"\"episode_reward_mean\",\n", |
||||
|
"\"episode_reward_max\",\n", |
||||
|
"\"episode_reward_min\",\n", |
||||
|
"\"episode_len_mean\",\n", |
||||
|
"]\n", |
||||
|
"pprint.pprint({k: v for k, v in best_result.metrics.items() if k in metrics_to_print})\n", |
||||
|
"\n", |
||||
|
" " |
||||
|
] |
||||
|
} |
||||
|
], |
||||
|
"metadata": { |
||||
|
"kernelspec": { |
||||
|
"display_name": "env", |
||||
|
"language": "python", |
||||
|
"name": "python3" |
||||
|
}, |
||||
|
"language_info": { |
||||
|
"codemirror_mode": { |
||||
|
"name": "ipython", |
||||
|
"version": 3 |
||||
|
}, |
||||
|
"file_extension": ".py", |
||||
|
"mimetype": "text/x-python", |
||||
|
"name": "python", |
||||
|
"nbconvert_exporter": "python", |
||||
|
"pygments_lexer": "ipython3", |
||||
|
"version": "3.10.12" |
||||
|
}, |
||||
|
"orig_nbformat": 4 |
||||
|
}, |
||||
|
"nbformat": 4, |
||||
|
"nbformat_minor": 2 |
||||
|
} |
||||
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