Normalization of observation updated to stay between -1 and 1

torch_training/training_navigation.py

@@ -3,7 +3,6 @@ from collections import deque
 
 import numpy as np
 import torch
-
 from dueling_double_dqn import Agent
 from flatland.envs.generators import complex_rail_generator
 from flatland.envs.rail_env import RailEnv
@@ -38,16 +37,16 @@ env = RailEnv(width=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 = RailEnv(width=10,
               height=20)
 env.load("./railway/complex_scene.pkl")
-
+"""
 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)
+              number_of_agents=1)
 env.reset(True, True)
 
 env_renderer = RenderTool(env, gl="PILSVG")
@@ -133,7 +132,7 @@ for trials in range(1, n_trials + 1):
         data, distance, agent_data = env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=7, current_depth=0)
         data = norm_obs_clip(data)
         distance = norm_obs_clip(distance)
-
+        agent_data = np.clip(agent_data, -1, 1)
         obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
     for i in range(2):
         time_obs.append(obs)
@@ -146,13 +145,12 @@ for trials in range(1, n_trials + 1):
     # Run episode
     for step in range(100):
         if demo:
-
-            env_renderer.renderEnv(show=True, show_observations=True)
+            env_renderer.renderEnv(show=True, show_observations=False)
         # print(step)
         # Action
         for a in range(env.get_num_agents()):
             if demo:
-                eps = 0
+                eps = 1
             # action = agent.act(np.array(obs[a]), eps=eps)
             action = agent.act(agent_obs[a], eps=eps)
             action_prob[action] += 1
@@ -165,6 +163,7 @@ for trials in range(1, n_trials + 1):
                                                         current_depth=0)
             data = norm_obs_clip(data)
             distance = norm_obs_clip(distance)
+            agent_data = np.clip(agent_data, -1, 1)
             next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
 
         time_obs.append(next_obs)