minor updates to training files

examples/training_navigation.py

@@ -11,38 +11,39 @@ np.random.seed(1)
 
 # Example generate a rail given a manual specification,
 # a map of tuples (cell_type, rotation)
-transition_probability = [5,  # empty cell - Case 0
-                          1,  # Case 1 - straight
+transition_probability = [15,  # empty cell - Case 0
+                          5,  # Case 1 - straight
                           5,  # Case 2 - simple switch
                           1,  # Case 3 - diamond crossing
                           1,  # Case 4 - single slip
                           1,  # Case 5 - double slip
                           1,  # Case 6 - symmetrical
                           0,  # Case 7 - dead end
-                          15,  # Case 1b (8)  - simple turn right
-                          15,  # Case 1c (9)  - simple turn left
-                          15]  # Case 2b (10) - simple switch mirrored
+                          1,  # Case 1b (8)  - simple turn right
+                          1,  # Case 1c (9)  - simple turn left
+                          1]  # Case 2b (10) - simple switch mirrored
 
 
 # Example generate a random rail
+"""
 env = RailEnv(width=10,
               height=10,
               rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
-              number_of_agents=3)
+              number_of_agents=1)
 """
-env = RailEnv(width=20,
-              height=20,
-              rail_generator=complex_rail_generator(nr_start_goal=20, min_dist=10, max_dist=99999, seed=0),
-              number_of_agents=5)
+env = RailEnv(width=15,
+              height=15,
+              rail_generator=complex_rail_generator(nr_start_goal=15, min_dist=5, max_dist=99999, seed=0),
+              number_of_agents=10)
 
 """
 env = RailEnv(width=20,
               height=20,
               rail_generator=rail_from_list_of_saved_GridTransitionMap_generator(
-                      ['../notebooks/testing_11.npy']),
-              number_of_agents=1)
-
+                      ['../notebooks/temp.npy']),
+              number_of_agents=3)
 
+"""
 env_renderer = RenderTool(env, gl="QT")
 handle = env.get_agent_handles()
 
@@ -125,7 +126,8 @@ for trials in range(1, n_trials + 1):
             next_obs[a] = np.clip(np.array(next_obs[a]) / norm, -1, 1)
         # Update replay buffer and train agent
         for a in range(env.number_of_agents):
-            agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
+            if not demo:
+                agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
             score += all_rewards[a]
 
         obs = next_obs.copy()

flatland/envs/generators.py

@@ -123,7 +123,7 @@ def complex_rail_generator(nr_start_goal=1, min_dist=2, max_dist=99999, seed=0):
                 # print("failed...")
                 created_sanity += 1
 
-        print("\n> Complex Rail Gen: Created #", len(start_goal), "pairs")
+        #print("\n> Complex Rail Gen: Created #", len(start_goal), "pairs")
         # print(start_goal)
 
         agents_position = [sg[0] for sg in start_goal]