Updated training to new state

torch_training/training_navigation.py

@@ -44,11 +44,11 @@ env = RailEnv(width=10,
               height=20)
 env.load("./railway/complex_scene.pkl")
 
-env = RailEnv(width=15,
-              height=15,
-              rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=10, min_dist=10, max_dist=99999, seed=0),
-              number_of_agents=1)
-env.reset(False, False)
+env = RailEnv(width=8,
+              height=8,
+              rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=5, min_dist=5, max_dist=99999, seed=0),
+              number_of_agents=2)
+env.reset(True, True)
 
 env_renderer = RenderTool(env, gl="PILSVG")
 handle = env.get_agent_handles()
@@ -70,11 +70,10 @@ action_prob = [0] * action_size
 agent_obs = [None] * env.get_num_agents()
 agent_next_obs = [None] * env.get_num_agents()
 agent = Agent(state_size, action_size, "FC", 0)
-#agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pth'))
+#agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint1500.pth'))
 
 demo = False
 
-
 def max_lt(seq, val):
     """
     Return greatest item in seq for which item < val applies.
@@ -145,35 +144,34 @@ for trials in range(1, n_trials + 1):
     score = 0
     env_done = 0
     # Run episode
-    for step in range(360):
+    for step in range(100):
         if demo:
 
-            env_renderer.renderEnv(show=True,show_observations=False)
+            env_renderer.renderEnv(show=True, show_observations=True)
         # print(step)
         # Action
         for a in range(env.get_num_agents()):
             if demo:
-                eps = 1
+                eps = 0
             # action = agent.act(np.array(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=7,
                                                         current_depth=0)
             data = norm_obs_clip(data)
             distance = norm_obs_clip(distance)
-            next_obs[a] = np.concatenate((np.concatenate((data, distance)),agent_data))
+            next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
 
         time_obs.append(next_obs)
 
         # Update replay buffer and train agent
         for a in range(env.get_num_agents()):
             agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
-
             if done[a]:
                 final_obs[a] = agent_obs[a].copy()
                 final_obs_next[a] = agent_next_obs[a].copy()
@@ -214,4 +212,4 @@ for trials in range(1, n_trials + 1):
                 action_prob / np.sum(action_prob)))
         torch.save(agent.qnetwork_local.state_dict(),
                    './Nets/avoid_checkpoint' + str(trials) + '.pth')
-        action_prob = [1] * 4
+        action_prob = [1] * action_size