using new utility functions

torch_training/training_navigation.py

@@ -43,7 +43,7 @@ env = RailEnv(width=15,
 """
 env = RailEnv(width=10,
               height=20, obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv()))
-env.load("./railway/flatland.pkl")
+env.load("./railway/complex_scene.pkl")
 file_load = True
 """
 
@@ -80,7 +80,7 @@ agent = Agent(state_size, action_size, "FC", 0)
 agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pth'))
 
 demo = True
-record_images = True
+record_images = False
 
 
 
@@ -129,15 +129,15 @@ for trials in range(1, n_trials + 1):
             if demo:
                 eps = 0
             # action = agent.act(np.array(obs[a]), eps=eps)
-            action = 2 #agent.act(agent_obs[a], eps=eps)
+            action = agent.act(agent_obs[a], eps=eps)
             action_prob[action] += 1
             action_dict.update({a: action})
         # Environment step
 
         next_obs, all_rewards, done, _ = env.step(action_dict)
         for a in range(env.get_num_agents()):
-            data, distance, agent_data = env.obs_builder.split_tree(tree=np.array(next_obs[a]), num_features_per_node=8,
-                                                        current_depth=0)
+            data, distance, agent_data = split_tree(tree=np.array(next_obs[a]), num_features_per_node=8,
+                                                    current_depth=0)
             data = norm_obs_clip(data)
             distance = norm_obs_clip(distance)
             agent_data = np.clip(agent_data, -1, 1)