changes according to refactoring of utils

examples/shields/rl/13_minigridsb.py

@@ -9,47 +9,50 @@ from minigrid.core.actions import Actions
 
 import time
 
-from utils import MiniGridShieldHandler, create_shield_query, parse_arguments, create_log_dir, ShieldingConfig
-from sb3utils import MiniGridSbShieldingWrapper
+from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig
+from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments
+
+GRID_TO_PRISM_BINARY="/home/spranger/research/tempestpy/Minigrid2PRISM/build/main"
 
 def mask_fn(env: gym.Env):
     return env.create_action_mask()
-    
+
 
 def main():
-    import argparse
-    args = parse_arguments(argparse)
-    
-    args.grid_path = F"{args.grid_path}.txt"
-    args.prism_path = F"{args.prism_path}.prism"
-    
-    shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
-    
+    args = parse_sb3_arguments()
+
+    formula = args.formula
+    shield_value = args.shield_value
+    shield_comparison = args.shield_comparison
+
+    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 = MiniGridSbShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query, mask_actions=args.shielding == ShieldingConfig.Full)
+    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))
-    
+
     steps = args.steps
-    
-    
+
+
     model.learn(steps)
- 
-  #W  mean_reward, std_reward = evaluate_policy(model, model.get_env())
-    
+
+
+    print("Learning done, hit enter")
+    input("")
     vec_env = model.get_env()
     obs = vec_env.reset()
     terminated = truncated = False
     while not terminated and not truncated:
         action_masks = None
         action, _states = model.predict(obs, action_masks=action_masks)
+        print(action)
         obs, reward, terminated, truncated, info = env.step(action)
         # action, _states = model.predict(obs, deterministic=True)
         # obs, rewards, dones, info = vec_env.step(action)
         vec_env.render("human")
         time.sleep(0.2)
-    
-    
+
+
 
 if __name__ == '__main__':
-    main()
+    main()