new training results added

torch_training/multi_agent_inference.py

@@ -17,7 +17,7 @@ from utils.observation_utils import normalize_observation
 random.seed(3)
 np.random.seed(2)
 
-file_name = "./railway/complex_scene.pkl"
+file_name = "./railway/simple_avoid.pkl"
 env = RailEnv(width=10,
               height=20,
               rail_generator=rail_from_file(file_name),
@@ -27,9 +27,9 @@ y_dim = env.height
 
 """
 
-x_dim = 10  # np.random.randint(8, 20)
-y_dim = 10  # np.random.randint(8, 20)
-n_agents = 5  # np.random.randint(3, 8)
+x_dim = 18  # np.random.randint(8, 20)
+y_dim = 14  # np.random.randint(8, 20)
+n_agents = 7  # np.random.randint(3, 8)
 n_goals = n_agents + np.random.randint(0, 3)
 min_dist = int(0.75 * min(x_dim, y_dim))
 
@@ -53,7 +53,7 @@ for i in range(tree_depth + 1):
 state_size = num_features_per_node * nr_nodes
 action_size = 5
 
-n_trials = 1
+n_trials = 10
 observation_radius = 10
 max_steps = int(3 * (env.height + env.width))
 eps = 1.
@@ -70,7 +70,7 @@ 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)
-with path(torch_training.Nets, "avoid_checkpoint52800.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint46200.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 record_images = False
@@ -102,7 +102,7 @@ for trials in range(1, n_trials + 1):
 
         next_obs, all_rewards, done, _ = env.step(action_dict)
         for a in range(env.get_num_agents()):
-            agent_obs[a] = agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
+            agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
 
         if done['__all__']:
             break