added py versions of notebooks

notebooks/FaultyActions.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# ## Example usage of Tempestpy
+
+# In[1]:
+
+
+from sb3_contrib import MaskablePPO
+from sb3_contrib.common.wrappers import ActionMasker
+from stable_baselines3.common.logger import Logger, CSVOutputFormat, TensorBoardOutputFormat, HumanOutputFormat
+
+import gymnasium as gym
+
+from minigrid.core.actions import Actions
+from minigrid.core.constants import TILE_PIXELS
+from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper
+
+import tempfile, datetime, shutil
+
+import time
+import os
+
+from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper, expname, shield_needed, shielded_evaluation, create_shield_overlay_image
+from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback
+
+import os, sys
+from copy import deepcopy
+
+from PIL import Image
+
+
+# In[3]:
+
+
+GRID_TO_PRISM_BINARY=os.getenv("M2P_BINARY")
+
+def mask_fn(env: gym.Env):
+    return env.create_action_mask()
+
+def nomask_fn(env: gym.Env):
+    return [1.0] * 7
+
+def main():
+    env = "MiniGrid-LavaFaultyS15-1-v0"
+    
+    formula = "Pmax=? [G ! AgentIsOnLava]"
+    value_for_training = 0.0
+    shield_comparison =  "absolute"
+    shielding = ShieldingConfig.Training
+    
+    logger = Logger("/tmp", output_formats=[HumanOutputFormat(sys.stdout)])
+    
+    env = gym.make(env, render_mode="rgb_array")
+    image_env = RGBImgObsWrapper(env, TILE_PIXELS)
+    env = RGBImgObsWrapper(env, 8)
+    env = ImgObsWrapper(env)
+    env = MiniWrapper(env)
+
+    
+    env.reset()
+    Image.fromarray(env.render()).show()
+
+    shield_values = [0.0, 0.9, 0.99, 0.999, 1.0]
+    shield_handlers = dict()
+    if shield_needed(shielding):
+        for value in shield_values: 
+            shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, "grid.txt", "grid.prism", formula, shield_value=value, shield_comparison=shield_comparison, nocleanup=False, prism_file=None)
+            env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
+            shield_handlers[value] = shield_handler
+
+    if shield_needed(shielding):
+        for value in shield_values:            
+            create_shield_overlay_image(image_env, shield_handlers[value].create_shield())
+            print(f"The shield for shield_value = {value}")
+
+
+    if shielding == ShieldingConfig.Training:
+        env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handlers[value_for_training], create_shield_at_reset=False)
+        env = ActionMasker(env, mask_fn)
+        print("Training with shield:")
+        create_shield_overlay_image(image_env, shield_handlers[value_for_training].create_shield())
+    elif shielding == ShieldingConfig.Disabled:
+        env = ActionMasker(env, nomask_fn)
+    else:
+        assert(False) 
+    model = MaskablePPO("CnnPolicy", env, verbose=1, device="auto")
+    model.set_logger(logger)
+    steps = 20_000
+
+    assert(False)
+    model.learn(steps,callback=[InfoCallback()])
+
+
+
+if __name__ == '__main__':
+    print("Starting the training")
+    main()
+
+
+# In[ ]:
+
+
+
+

notebooks/HelloLavaGap.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# ## Example usage of Tempestpy
+
+# In[1]:
+
+
+from sb3_contrib import MaskablePPO
+from sb3_contrib.common.wrappers import ActionMasker
+from stable_baselines3.common.logger import Logger, CSVOutputFormat, TensorBoardOutputFormat, HumanOutputFormat
+
+import gymnasium as gym
+
+from minigrid.core.actions import Actions
+from minigrid.core.constants import TILE_PIXELS
+from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper
+
+import tempfile, datetime, shutil
+
+import time
+import os
+
+from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper, expname, shield_needed, shielded_evaluation, create_shield_overlay_image
+from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback
+
+import os, sys
+from copy import deepcopy
+
+from PIL import Image
+
+
+# In[ ]:
+
+
+GRID_TO_PRISM_BINARY=os.getenv("M2P_BINARY")
+
+def mask_fn(env: gym.Env):
+    return env.create_action_mask()
+
+def nomask_fn(env: gym.Env):
+    return [1.0] * 7
+
+def main():
+    env = "MiniGrid-LavaGapS6-v0"
+
+    # TODO Change the safety specification
+    formula = "Pmax=? [G !AgentIsOnLava]"
+    value_for_training = 1.0
+    shield_comparison =  "absolute"
+    shielding = ShieldingConfig.Training
+    
+    logger = Logger("/tmp", output_formats=[HumanOutputFormat(sys.stdout)])
+    
+
+    env = gym.make(env, render_mode="rgb_array")
+    image_env = RGBImgObsWrapper(env, TILE_PIXELS)
+    env = RGBImgObsWrapper(env, 8)
+    env = ImgObsWrapper(env)
+    env = MiniWrapper(env)
+
+    
+    env.reset()
+    Image.fromarray(env.render()).show()
+    input("") 
+    
+    shield_handlers = dict()
+    if shield_needed(shielding):
+        for value in [0.0, 1.0]:
+            shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, "grid.txt", "grid.prism", formula, shield_value=value, shield_comparison=shield_comparison, nocleanup=True, prism_file=None)
+            env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
+            shield_handlers[value] = shield_handler
+
+    print("Symbolic Description of the Model:")
+    shield_handlers[1.0].print_symbolic_model()
+    input("")
+
+    if shield_needed(shielding):
+        for value in [1.0]:
+            create_shield_overlay_image(image_env, shield_handlers[value].create_shield())
+            print(f"The shield for shield_value = {value}")
+            input("")
+    
+    if shielding == ShieldingConfig.Training:
+        env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
+        env = ActionMasker(env, mask_fn)
+        print("Training with shield:")
+        create_shield_overlay_image(image_env, shield_handlers[value_for_training].create_shield())
+    elif shielding == ShieldingConfig.Disabled:
+        env = ActionMasker(env, nomask_fn)
+    else:
+        assert(False) 
+    model = MaskablePPO("CnnPolicy", env, verbose=1, device="auto")
+    model.set_logger(logger)
+    steps = 20_000
+
+ 
+    model.learn(steps,callback=[InfoCallback()])
+
+
+
+if __name__ == '__main__':
+    main()
+
+
+# In[ ]:
+
+
+
+

notebooks/Playground.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# ## Example usage of Tempestpy
+
+# In[1]:
+
+
+from sb3_contrib import MaskablePPO
+from sb3_contrib.common.wrappers import ActionMasker
+from stable_baselines3.common.logger import Logger, CSVOutputFormat, TensorBoardOutputFormat, HumanOutputFormat
+
+import gymnasium as gym
+
+from minigrid.core.actions import Actions
+from minigrid.core.constants import TILE_PIXELS
+from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper
+
+import tempfile, datetime, shutil
+
+import time
+import os
+
+from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper, expname, shield_needed, shielded_evaluation, create_shield_overlay_image
+from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback
+
+import os, sys
+from copy import deepcopy
+
+from PIL import Image
+
+
+# In[ ]:
+
+
+GRID_TO_PRISM_BINARY=os.getenv("M2P_BINARY")
+import gymnasium as gym
+
+
+def mask_fn(env: gym.Env):
+    return env.create_action_mask()
+
+def nomask_fn(env: gym.Env):
+    return [1.0] * 7
+
+def main():
+    # Edit 'environments/Minigrid/minigrid/envs/Playground.py' to alter the environment
+    env = "MiniGrid-Playground-v0"
+
+    # TODO Change the safety specification
+    formula = "Pmax=? [G !AgentIsOnLava]"
+    value_for_training = 1.0
+    shield_comparison =  "absolute"
+    shielding = ShieldingConfig.Training
+    
+    logger = Logger("/tmp", output_formats=[HumanOutputFormat(sys.stdout)])
+    
+
+    env = gym.make(env, render_mode="rgb_array")
+    image_env = RGBImgObsWrapper(env, TILE_PIXELS)
+    env = RGBImgObsWrapper(env, 8)
+    env = ImgObsWrapper(env)
+    env = MiniWrapper(env)
+
+    
+    env.reset()
+    Image.fromarray(env.render()).show()
+        
+    shield_handlers = dict()
+    if shield_needed(shielding):
+        for value in [0.9, 0.99, 0.999, 0.9999, 1.0]:
+            shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, "grid.txt", "grid.prism", formula, shield_value=value, shield_comparison=shield_comparison, nocleanup=True, prism_file=None)
+            env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
+            create_shield_overlay_image(image_env, shield_handler.create_shield())
+            print(f"The shield for shield_value = {value}")
+
+            shield_handlers[value] = shield_handler
+
+
+    if shielding == ShieldingConfig.Training:
+        env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
+        env = ActionMasker(env, mask_fn)
+        print("Training with shield:")
+        create_shield_overlay_image(image_env, shield_handlers[value_for_training].create_shield())
+    elif shielding == ShieldingConfig.Disabled:
+        env = ActionMasker(env, nomask_fn)
+    else:
+        assert(False) 
+    model = MaskablePPO("CnnPolicy", env, verbose=1, device="auto")
+    model.set_logger(logger)
+    steps = 20_000
+
+ 
+    model.learn(steps,callback=[InfoCallback()])
+
+
+
+if __name__ == '__main__':
+    print("Starting the training")
+    main()
+
+
+# In[ ]:
+
+
+
+

notebooks/SlipperyCliff.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# ## Example usage of Tempestpy
+
+# In[1]:
+
+
+from sb3_contrib import MaskablePPO
+from sb3_contrib.common.wrappers import ActionMasker
+from stable_baselines3.common.logger import Logger, CSVOutputFormat, TensorBoardOutputFormat, HumanOutputFormat
+
+import gymnasium as gym
+
+from minigrid.core.actions import Actions
+from minigrid.core.constants import TILE_PIXELS
+from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper
+
+import tempfile, datetime, shutil
+
+import time
+import os
+
+from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper, expname, shield_needed, shielded_evaluation, create_shield_overlay_image
+from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback
+
+import os, sys
+from copy import deepcopy
+
+from PIL import Image
+
+
+# In[3]:
+
+
+GRID_TO_PRISM_BINARY=os.getenv("M2P_BINARY")
+
+def mask_fn(env: gym.Env):
+    return env.create_action_mask()
+
+def nomask_fn(env: gym.Env):
+    return [1.0] * 7
+
+def main():
+    #env = "MiniGrid-LavaSlipperyCliff-16x13-Slip10-Time-v0"
+    env = "MiniGrid-WindyCity2-v0"
+    
+    formula = "Pmax=? [G ! AgentIsOnLava]"
+    value_for_training = 0.99
+    shield_comparison =  "absolute"
+    shielding = ShieldingConfig.Training
+    
+    logger = Logger("/tmp", output_formats=[HumanOutputFormat(sys.stdout)])
+    
+    env = gym.make(env, render_mode="rgb_array")
+    image_env = RGBImgObsWrapper(env, TILE_PIXELS)
+    env = RGBImgObsWrapper(env, 8)
+    env = ImgObsWrapper(env)
+    env = MiniWrapper(env)
+
+    
+    env.reset()
+    Image.fromarray(env.render()).show()
+    
+    shield_handlers = dict()
+    if shield_needed(shielding):
+        for value in [0.9, 0.95, 0.99, 1.0]:
+            shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, "grid.txt", "grid.prism", formula, shield_value=value, shield_comparison=shield_comparison, nocleanup=True, prism_file=None)
+            env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
+
+
+            shield_handlers[value] = shield_handler
+    if shield_needed(shielding):
+        for value in [0.9, 0.95, 0.99, 1.0]:            
+            create_shield_overlay_image(image_env, shield_handlers[value].create_shield())
+            print(f"The shield for shield_value = {value}")
+
+    if shielding == ShieldingConfig.Training:
+        env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handlers[value_for_training], create_shield_at_reset=False)
+        env = ActionMasker(env, mask_fn)
+        print("Training with shield:")
+        create_shield_overlay_image(image_env, shield_handlers[value_for_training].create_shield())
+    elif shielding == ShieldingConfig.Disabled:
+        env = ActionMasker(env, nomask_fn)
+    else:
+        assert(False) 
+    model = MaskablePPO("CnnPolicy", env, verbose=1, device="auto")
+    model.set_logger(logger)
+    steps = 20_000
+
+    assert(False)
+    model.learn(steps,callback=[InfoCallback()])
+
+
+
+if __name__ == '__main__':
+    print("Starting the training")
+    main()
+
+
+# In[ ]:
+
+
+
+