minor bugfix in inference files

torch_training/multi_agent_inference.py

@@ -53,6 +53,7 @@ state_size = num_features_per_node * nr_nodes
 action_size = 5
 
 n_trials = 100
+observation_radius = 10
 max_steps = int(3 * (env.height + env.width))
 eps = 1.
 eps_end = 0.005
@@ -68,7 +69,7 @@ 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_checkpoint2900.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint49700.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 record_images = False
@@ -84,7 +85,7 @@ for trials in range(1, n_trials + 1):
     for a in range(env.get_num_agents()):
         data, distance, agent_data = split_tree(tree=np.array(obs[a]), num_features_per_node=num_features_per_node,
                                                 current_depth=0)
-        data = norm_obs_clip(data)
+        data = norm_obs_clip(data, fixed_radius=observation_radius)
         distance = norm_obs_clip(distance)
         agent_data = np.clip(agent_data, -1, 1)
         agent_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
@@ -106,9 +107,10 @@ 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()):
-            data, distance, agent_data = split_tree(tree=np.array(obs[a]), num_features_per_node=num_features_per_node,
+            data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
+                                                    num_features_per_node=num_features_per_node,
                                                     current_depth=0)
-            data = norm_obs_clip(data)
+            data = norm_obs_clip(data, fixed_radius=observation_radius)
             distance = norm_obs_clip(distance)
             agent_data = np.clip(agent_data, -1, 1)
             agent_next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))

torch_training/render_agent_behavior.py

@@ -3,7 +3,7 @@ from collections import deque
 
 import numpy as np
 import torch
-from flatland.envs.generators import rail_from_file
+from flatland.envs.generators import complex_rail_generator
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
@@ -16,7 +16,7 @@ from utils.observation_utils import norm_obs_clip, split_tree
 
 random.seed(1)
 np.random.seed(1)
-
+"""
 file_name = "./railway/complex_scene.pkl"
 env = RailEnv(width=10,
               height=20,
@@ -40,7 +40,7 @@ env = RailEnv(width=x_dim,
               obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
               number_of_agents=n_agents)
 env.reset(True, True)
-"""
+
 observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
 env_renderer = RenderTool(env, gl="PILSVG", )
 num_features_per_node = env.obs_builder.observation_dim
@@ -67,7 +67,7 @@ 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_checkpoint60000.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint49700.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 record_images = False
@@ -101,7 +101,7 @@ 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=False)
+        env_renderer.render_env(show=True, show_observations=False, show_predictions=True)
 
         if record_images:
             env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))