changes in sb3 rl training

- included callbacks for initial image and info plotting
- switched to CnnPolicy
- changed GRID_TO_PRISM_BINARY to environment var M2P_BINARY

examples/shields/rl/13_minigridsb.py

@@ -1,19 +1,20 @@
 from sb3_contrib import MaskablePPO
 from sb3_contrib.common.maskable.evaluation import evaluate_policy
-from sb3_contrib.common.maskable.policies import MaskableActorCriticPolicy
 from sb3_contrib.common.wrappers import ActionMasker
 
 import gymnasium as gym
 
 from minigrid.core.actions import Actions
+from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper
 
 import time
 
-from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig
-from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments
+from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper
+from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback
 
-GRID_TO_PRISM_BINARY="/home/spranger/research/tempestpy/Minigrid2PRISM/build/main"
+import os
 
+GRID_TO_PRISM_BINARY=os.getenv("M2P_BINARY")
 def mask_fn(env: gym.Env):
     return env.create_action_mask()
 
@@ -27,14 +28,16 @@ def main():
 
     shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, args.grid_file, args.prism_output_file, formula, shield_value=shield_value, shield_comparison=shield_comparison)
     env = gym.make(args.env, render_mode="rgb_array")
+    env = RGBImgObsWrapper(env) # Get pixel observations
+    env = ImgObsWrapper(env) # Get rid of the 'mission' field
+    env = MiniWrapper(env)
     env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False, mask_actions=args.shielding == ShieldingConfig.Full)
     env = ActionMasker(env, mask_fn)
-    model = MaskablePPO(MaskableActorCriticPolicy, env, gamma=0.4, verbose=1, tensorboard_log=create_log_dir(args))
+    model = MaskablePPO("CnnPolicy", env, verbose=1, tensorboard_log=create_log_dir(args))
 
     steps = args.steps
 
-
-    model.learn(steps)
+    model.learn(steps,callback=[ImageRecorderCallback(), InfoCallback()], log_interval=1)
 
 
     print("Learning done, hit enter")