using qt renderer for visualization of training

examples/training_navigation.py

@@ -10,21 +10,21 @@ np.random.seed(1)
 
 # Example generate a rail given a manual specification,
 # a map of tuples (cell_type, rotation)
-transition_probability = [0.5,  # empty cell - Case 0
-                          1.0,  # Case 1 - straight
-                          1.0,  # Case 2 - simple switch
-                          0.3,  # Case 3 - diamond crossing
-                          0.5,  # Case 4 - single slip
-                          0.5,  # Case 5 - double slip
-                          0.2,  # Case 6 - symmetrical
-                          0.0]  # Case 7 - dead end
+transition_probability = [5,  # empty cell - Case 0
+                          15,  # 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
 
 # Example generate a random rail
-env = RailEnv(width=20,
-              height=20,
+env = RailEnv(width=10,
+              height=10,
               rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
-              number_of_agents=1)
-env_renderer = RenderTool(env)
+              number_of_agents=3)
+env_renderer = RenderTool(env, gl="QT")
 handle = env.get_agent_handles()
 
 state_size = 105
@@ -42,7 +42,7 @@ action_prob = [0]*4
 agent = Agent(state_size, action_size, "FC", 0)
 agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint15000.pth'))
 
-demo = True
+demo = False
 def max_lt(seq, val):
     """
     Return greatest item in seq for which item < val applies.
@@ -74,7 +74,7 @@ for trials in range(1, n_trials + 1):
     # Reset environment
     obs = env.reset()
     for a in range(env.number_of_agents):
-        norm = max(1, max_lt(obs[a],np.inf))
+        norm = max(1, max_lt(obs[a], np.inf))
         obs[a] = np.clip(np.array(obs[a]) / norm, -1, 1)
 
     # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
@@ -83,7 +83,7 @@ for trials in range(1, n_trials + 1):
     env_done = 0
 
     # Run episode
-    for step in range(50):
+    for step in range(100):
         if demo:
             env_renderer.renderEnv(show=True)
         #print(step)