added dqn algorithm

examples/shields/rl/11_minigridrl.py

@@ -30,7 +30,6 @@ from ray.rllib.utils.test_utils import check_learning_achieved, framework_iterat
 from ray import tune, air
 from ray.rllib.algorithms.callbacks import DefaultCallbacks
 from ray.tune.logger import pretty_print
-from ray.rllib.algorithms import ppo
 from ray.rllib.models import ModelCatalog
 
 from ray.rllib.utils.torch_utils import FLOAT_MIN
@@ -196,14 +195,15 @@ def create_environment(args):
     return env
 
 
-def register_custom_minigrid_env():
+def register_custom_minigrid_env(args):
     env_name = "mini-grid"
     register_env(env_name, env_creater_custom)
+
     ModelCatalog.register_custom_model(
         "pa_model", 
         TorchActionMaskModel
     )
-
+        
 def create_shield_dict(args):
     env = create_environment(args)
     # print(env.pprint_grid())
@@ -216,7 +216,7 @@ def create_shield_dict(args):
     shield_dict = create_shield(grid_file, prism_path)
     #shield_dict = {state.id : shield.get_choice(state).choice_map for state in model.states}
    
-    print(F"Shield dictionary {shield_dict}")
+    #print(F"Shield dictionary {shield_dict}")
     # for state_id in model.states:
     #     choices = shield.get_choice(state_id)
     #     print(F"Allowed choices in state {state_id}, are {choices.choice_map} ")
@@ -228,7 +228,7 @@ def ppo(args):
     ray.init(num_cpus=3)
 
     
-    register_custom_minigrid_env()
+    register_custom_minigrid_env(args)
     shield_dict = create_shield_dict(args)
     
     config = (PPOConfig()
@@ -268,30 +268,35 @@ def ppo(args):
 
 
 def dqn(args):
-    config = DQNConfig()
-    register_custom_minigrid_env()
+    register_custom_minigrid_env(args)
     shield_dict = create_shield_dict(args)
-    replay_config = config.replay_buffer_config.update(
-        {
-            "capacity": 60000,
-            "prioritized_replay_alpha": 0.5,
-            "prioritized_replay_beta": 0.5,
-            "prioritized_replay_eps": 3e-6,
-        }
-    )
+
     
-    config = config.training(replay_buffer_config=replay_config, model={    
-            "custom_model": "pa_model",
-            "custom_model_config" : {"shield": shield_dict, "no_masking": args.no_masking}
-    })
+    config = DQNConfig()
     config = config.resources(num_gpus=0)
     config = config.rollouts(num_rollout_workers=1)
+    config = config.environment(env="mini-grid", env_config={"shield": shield_dict })
     config = config.framework("torch")
     config = config.callbacks(MyCallbacks)
     config = config.rl_module(_enable_rl_module_api = False)
+    config = config.training(hiddens=[], dueling=False, model={    
+            "custom_model": "pa_model",
+            "custom_model_config" : {"shield": shield_dict, "no_masking": args.no_masking}
+    })
     
-    config = config.environment(env="mini-grid", env_config={"shield": shield_dict })
-    
+    algo = (
+        config.build()
+    )
+         
+    for i in range(30):
+        result = algo.train()
+        print(pretty_print(result))
+
+        if i % 5 == 0:
+            checkpoint_dir = algo.save()
+            print(f"Checkpoint saved in directory {checkpoint_dir}")
+            
+    ray.shutdown()
 
 
 def main():

examples/shields/rl/MaskModels.py

@@ -1,5 +1,4 @@
 from typing import Dict, Optional, Union
-
 from ray.rllib.algorithms.dqn.dqn_torch_model import DQNTorchModel
 from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
 from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
@@ -75,6 +74,8 @@ class TorchActionMaskModel(TorchModelV2, nn.Module):
         # assert(False)
         # Convert action_mask into a [0.0 || -inf]-type mask.
         inf_mask = torch.clamp(torch.log(action_mask), min=FLOAT_MIN)
+        # print(F"Logits Size: {logits.size()} Inf-Mask Size: {inf_mask.size()}")
+        # print(F"Logits:{logits} Inf-Mask: {inf_mask}")
         masked_logits = logits + inf_mask
 
        # print(F"Infinity mask {inf_mask}, Masked logits {masked_logits}")

examples/shields/rl/Wrapper.py

@@ -109,7 +109,7 @@ class MiniGridEnvWrapper(gym.core.Wrapper):
         # else set everything to one
         mask = np.array([0.0] * self.max_available_actions, dtype=np.int8)
         
-        if cur_pos_str in self.shield:
+        if cur_pos_str in self.shield and self.shield[cur_pos_str]:
              allowed_actions = self.shield[cur_pos_str]
              for allowed_action in allowed_actions:
                  index = allowed_action[0]
@@ -118,8 +118,8 @@ class MiniGridEnvWrapper(gym.core.Wrapper):
             for index, x in enumerate(mask):
                 mask[index] = 1.0
             
-            
-        #print(F"Action Mask for position {coordinates} and view {view_direction} is {mask}")
+   
+        #print(F"Action Mask for position {coordinates} and view {view_direction} is {mask} Position String: {cur_pos_str})")
     
         return mask