new training results

torch_training/multi_agent_inference.py

@@ -17,7 +17,7 @@ from utils.observation_utils import norm_obs_clip, split_tree
 random.seed(3)
 np.random.seed(2)
 
-file_name = "./railway/navigate_and_avoid.pkl"
+file_name = "./railway/complex_scene.pkl"
 env = RailEnv(width=10,
               height=20,
               rail_generator=rail_from_file(file_name),
@@ -27,9 +27,9 @@ y_dim = env.height
 
 """
 
-x_dim = np.random.randint(8, 20)
-y_dim = np.random.randint(8, 20)
-n_agents = np.random.randint(3, 8)
+x_dim = 20 #np.random.randint(8, 20)
+y_dim = 20 #np.random.randint(8, 20)
+n_agents = 10 #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 = 5
+n_trials = 1
 observation_radius = 10
 max_steps = int(3 * (env.height + env.width))
 eps = 1.
@@ -70,10 +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)
-with path(torch_training.Nets, "avoid_checkpoint53400.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint60000.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
-record_images = False
+record_images = True
 frame_step = 0
 
 for trials in range(1, n_trials + 1):
@@ -93,7 +93,7 @@ for trials in range(1, n_trials + 1):
 
     # Run episode
     for step in range(max_steps):
-        env_renderer.render_env(show=True, show_observations=False, show_predictions=True)
+        env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
 
         if record_images:
             env_renderer.gl.save_image("./Images/Avoiding/flatland_frame_{:04d}.bmp".format(frame_step))