added new special maps

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/complex_scene.pkl")
+env.load("./railway/flatland.pkl")
 file_load = True
 """
 
@@ -79,7 +79,7 @@ agent = Agent(state_size, action_size, "FC", 0)
 agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pth'))
 
 demo = True
-record_images = False
+record_images = True
 
 def max_lt(seq, val):
     """
@@ -140,6 +140,7 @@ for trials in range(1, n_trials + 1):
     final_obs = obs.copy()
     final_obs_next = obs.copy()
     for a in range(env.get_num_agents()):
+        print(a)
         data, distance, agent_data = env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=8,
                                                                 current_depth=0)
         data = norm_obs_clip(data)
@@ -164,14 +165,14 @@ for trials in range(1, n_trials + 1):
         if demo:
             env_renderer.renderEnv(show=True, show_observations=False)
             if record_images:
-                env_renderer.gl.saveImage("./Images/frame_{:04d}.bmp".format(step))
+                env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(step))
         # print(step)
         # Action
         for a in range(env.get_num_agents()):
             if demo:
                 eps = 0
             # action = agent.act(np.array(obs[a]), eps=eps)
-            action = agent.act(agent_obs[a], eps=eps)
+            action = 2 #agent.act(agent_obs[a], eps=eps)
             action_prob[action] += 1
             action_dict.update({a: action})
         # Environment step