arguments and log dir

examples/shields/rl/11_minigridrl.py

@@ -8,7 +8,7 @@ from ray.rllib.utils.typing import PolicyID
 import stormpy
 import stormpy.core
 import stormpy.simulator
-
+from datetime import datetime
 
 import stormpy.shields
 import stormpy.logic
@@ -35,7 +35,6 @@ from ray.rllib.models import ModelCatalog
 from ray.rllib.utils.torch_utils import FLOAT_MIN
 
 from ray.rllib.models.preprocessors import get_preprocessor
-from MaskEnvironments import ParametricActionsMiniGridEnv
 from MaskModels import TorchActionMaskModel
 from Wrapper import OneHotWrapper, MiniGridEnvWrapper
 
@@ -76,14 +75,32 @@ class MyCallbacks(DefaultCallbacks):
 def parse_arguments(argparse):
     parser = argparse.ArgumentParser()
     # parser.add_argument("--env", help="gym environment to load", default="MiniGrid-Empty-8x8-v0")
-    parser.add_argument("--env", help="gym environment to load", default="MiniGrid-LavaCrossingS9N1-v0")
-    parser.add_argument("--seed", type=int, help="seed for environment", default=1)
-    parser.add_argument("--tile_size", type=int, help="size at which to render tiles", default=32)
-    parser.add_argument("--agent_view", default=False, action="store_true", help="draw the agent sees")
+    parser.add_argument("--env", 
+                        help="gym environment to load", 
+                        default="MiniGrid-LavaCrossingS9N1-v0", 
+                        choices=[
+                                "MiniGrid-LavaCrossingS9N1-v0",
+                                "MiniGrid-DoorKey-8x8-v0", 
+                                "MiniGrid-Dynamic-Obstacles-8x8-v0",
+                                "MiniGrid-Empty-Random-6x6-v0",
+                                "MiniGrid-Fetch-6x6-N2-v0", 
+                                "MiniGrid-FourRooms-v0", 
+                                "MiniGrid-KeyCorridorS6R3-v0", 
+                                "MiniGrid-GoToDoor-8x8-v0",
+                                "MiniGrid-LavaGapS7-v0",
+                                "MiniGrid-SimpleCrossingS9N3-v0",
+                                "MiniGrid-BlockedUnlockPickup-v0",
+                                "MiniGrid-LockedRoom-v0",
+                                "MiniGrid-ObstructedMaze-1Dlh-v0",
+                                "MiniGrid-DoorKey-16x16-v0",
+                                "MiniGrid-RedBlueDoors-6x6-v0",])
+    
+   # parser.add_argument("--seed", type=int, help="seed for environment", default=None)
     parser.add_argument("--grid_path", default="Grid.txt")
     parser.add_argument("--prism_path", default="Grid.PRISM")
     parser.add_argument("--no_masking", default=False)
     parser.add_argument("--algorithm", default="ppo", choices=["ppo", "dqn"])
+    parser.add_argument("--log_dir", default="../log_results/")
     
     args = parser.parse_args()
     
@@ -91,12 +108,8 @@ def parse_arguments(argparse):
 
 
 def env_creater_custom(config):
-    # name = config.get("name", "MiniGrid-LavaCrossingS9N1-v0")
-    # # name = config.get("name", "MiniGrid-Empty-8x8-v0")
     framestack = config.get("framestack", 4)
     shield = config.get("shield", {})
-    # env = gym.make(name)
-    # env = ParametricActionsMiniGridEnv(config)
     name = config.get("name", "MiniGrid-LavaCrossingS9N1-v0")
     framestack = config.get("framestack", 4)
     
@@ -109,14 +122,6 @@ def env_creater_custom(config):
                         framestack=framestack
                         )
     
-    # obs = env.observation_space.sample()
-    # obs2, infos = env.reset(seed=None, options={})
-    
-    # print(F"Obs is {obs} before reset. After reset: {obs2}")
-    # env = minigrid.wrappers.RGBImgPartialObsWrapper(env)
-    
-    # print(F"Created Custom Minigrid Environment is {env}")
-
     return env
 
 def env_creater_cart(config):
@@ -124,11 +129,9 @@ def env_creater_cart(config):
 
 def env_creater(config):
     name = config.get("name", "MiniGrid-LavaCrossingS9N1-v0")
-    # name = config.get("name", "MiniGrid-Empty-8x8-v0")
     framestack = config.get("framestack", 4)
     
     env = gym.make(name)
-    # env = minigrid.wrappers.RGBImgPartialObsWrapper(env)
     env = minigrid.wrappers.ImgObsWrapper(env)
     env = OneHotWrapper(env,
                         config.vector_index if hasattr(config, "vector_index") else 0,
@@ -151,6 +154,7 @@ def create_shield(grid_file, prism_path):
     
     program = stormpy.parse_prism_program(prism_path)
     formula_str = "Pmax=? [G !\"AgentIsInLavaAndNotDone\"]"
+    #formula_str = "Pmax=? [G \"AgentIsInGoalAndNotDone\"]"
     
     formulas = stormpy.parse_properties_for_prism_program(formula_str, program)
     options = stormpy.BuilderOptions([p.raw_formula for p in formulas])
@@ -238,15 +242,15 @@ def ppo(args):
         .framework("torch")       
         .callbacks(MyCallbacks)
         .rl_module(_enable_rl_module_api = False)
+        .debugging(logger_config={
+            "type": "ray.tune.logger.TBXLogger", 
+            "logdir": F"{args.log_dir}{datetime.now()}-{args.algorithm}"
+        })
         .training(_enable_learner_api=False ,model={
             "custom_model": "pa_model",
             "custom_model_config" : {"shield": shield_dict, "no_masking": args.no_masking}            
-            # "fcnet_hiddens": [256,256],
-            # "fcnet_activation": "relu",
-            
         }))
     
-    
     algo =(
         
         config.build()
@@ -279,6 +283,10 @@ def dqn(args):
     config = config.framework("torch")
     config = config.callbacks(MyCallbacks)
     config = config.rl_module(_enable_rl_module_api = False)
+    config = config.debugging(logger_config={
+            "type": "ray.tune.logger.TBXLogger", 
+            "logdir": F"{args.log_dir}{datetime.now()}-{args.algorithm}"
+        })
     config = config.training(hiddens=[], dueling=False, model={    
             "custom_model": "pa_model",
             "custom_model_config" : {"shield": shield_dict, "no_masking": args.no_masking}

examples/shields/rl/MaskEnvironments.py

@@ -1,91 +0,0 @@
-import random
-import minigrid
-
-import gymnasium as gym
-import numpy as np
-from gymnasium.spaces import Box, Dict, Discrete
-from Wrapper import OneHotWrapper
-
-
-class ParametricActionsMiniGridEnv(gym.Env):
-    """Parametric action version of MiniGrid.
-
-    """
-
-    def __init__(self, config):
-       
-        name = config.get("name", "MiniGrid-LavaCrossingS9N1-v0")
-        self.left_action_embed = np.random.randn(2)
-        self.right_action_embed = np.random.randn(2)
-        framestack = config.get("framestack", 4)
-        
-        # env = gym.make(name)
-        # env = minigrid.wrappers.ImgObsWrapper(env)
-        # env = OneHotWrapper(env,
-        #                 config.vector_index if hasattr(config, "vector_index") else 0,
-        #                 framestack=framestack
-        #                 )
-        self.wrapped = gym.make(name)
-        # self.observation_space = Dict(
-        #     {
-        #          "action_mask": None,
-        #          "avail_actions": None,
-        #         "cart": self.wrapped.observation_space,
-        #     }
-        # )
-        print(F"Wrapped environment is {self.wrapped}")
-        self.step_count = 0
-        self.action_space = self.wrapped.action_space
-        self.observation_space = self.wrapped.observation_space
-        
-        
-    def update_avail_actions(self):
-        self.action_assignments = np.array(
-            [[0.0, 0.0]] * self.action_space.n, dtype=np.float32
-        )
-        self.action_mask = np.array([0.0] * self.action_space.n, dtype=np.int8)
-        self.left_idx, self.right_idx = random.sample(range(self.action_space.n), 2)
-        self.action_assignments[self.left_idx] = self.left_action_embed
-        self.action_assignments[self.right_idx] = self.right_action_embed
-        self.action_mask[self.left_idx] = 1
-        self.action_mask[self.right_idx] = 1
-
-    def reset(self, *, seed=None, options=None):
-        self.update_avail_actions()
-        obs, infos = self.wrapped.reset()
-        return obs, infos
-        return {
-            "action_mask": self.action_mask,
-            "avail_action": self.action_assignments,
-            "cart": obs,
-        }, infos
-
-    def step(self, action):
-        if action == self.left_idx:
-            actual_action = 0
-        elif action == self.right_idx:
-            actual_action = 1
-        else:
-            actual_action = 0
-            # raise ValueError(
-            #     "Chosen action was not one of the non-zero action embeddings",
-            #     action,
-            #     self.action_assignments,
-            #     self.action_mask,
-            #     self.left_idx,
-            #     self.right_idx,
-            # )
-        orig_obs, rew, done, truncated, info = self.wrapped.step(actual_action)
-        self.update_avail_actions()
-        self.action_mask = self.action_mask.astype(np.int8)
-        print(F"Info is {info}")
-        info["Hello" : "Ich kenn mich nix aus"]
-        return orig_obs, rew, done, truncated, info
-        obs = {
-            "action_mask": self.action_mask,
-            "action_mask": self.action_assignments,
-            "cart": orig_obs,
-        }
-        return obs, rew, done, truncated, info
-
-   

examples/shields/rl/Wrapper.py

@@ -82,7 +82,7 @@ class OneHotWrapper(gym.core.ObservationWrapper):
 
 
 class MiniGridEnvWrapper(gym.core.Wrapper):
-    def __init__(self, env, shield):
+    def __init__(self, env, shield={}):
         super(MiniGridEnvWrapper, self).__init__(env)
         self.max_available_actions = env.action_space.n
         self.observation_space = Dict(
@@ -91,7 +91,6 @@ class MiniGridEnvWrapper(gym.core.Wrapper):
                 "action_mask" : Box(0, 10, shape=(self.max_available_actions,), dtype=np.int8),
             }
         )
-        
         self.shield = shield
         
         
@@ -124,7 +123,7 @@ class MiniGridEnvWrapper(gym.core.Wrapper):
         return mask
     
     def reset(self, *, seed=None, options=None):
-        obs, infos = self.env.reset()
+        obs, infos = self.env.reset(seed=seed, options=options)
         mask = self.create_action_mask()
         return {
             "data": obs["image"],