resolved issues

examples/flatland_2_0_example.py

@@ -8,7 +8,7 @@ from flatland.utils.rendertools import RenderTool
 
 np.random.seed(1)
 
-# Use the complex_rail_generator to generate feasible network configurations with corresponding tasks
+# Use the new sparse_rail_generator to generate feasible network configurations with corresponding tasks
 # Training on simple small tasks is the best way to get familiar with the environment
 
 # Use a the malfunction generator to break agents from time to time
@@ -22,7 +22,7 @@ TreeObservation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictor
 env = RailEnv(width=20,
               height=20,
               rail_generator=sparse_rail_generator(num_cities=2,  # Number of cities in map (where train stations are)
-                                                   num_intersections=1,  # Number of interesections (no start / target)
+                                                   num_intersections=1,  # Number of intersections (no start / target)
                                                    num_trainstations=15,  # Number of possible start/targets on map
                                                    min_node_dist=3,  # Minimal distance of nodes
                                                    node_radius=3,  # Proximity of stations to city center
@@ -32,16 +32,14 @@ env = RailEnv(width=20,
                                                    enhance_intersection=True
                                                    ),
               number_of_agents=5,
-              stochastic_data=stochastic_data,  # Malfunction generator data
+              stochastic_data=stochastic_data,  # Malfunction data generator
               obs_builder_object=TreeObservation)
 
 env_renderer = RenderTool(env, gl="PILSVG", )
 
 
 # Import your own Agent or use RLlib to train agents on Flatland
-# As an example we use a random agent here
-
-
+# As an example we use a random agent instead
 class RandomAgent:
 
     def __init__(self, state_size, action_size):
@@ -76,48 +74,46 @@ class RandomAgent:
 # Initialize the agent with the parameters corresponding to the environment and observation_builder
 # Set action space to 4 to remove stop action
 agent = RandomAgent(218, 4)
-n_trials = 1
+
 
 # Empty dictionary for all agent action
 action_dict = dict()
-print("Starting Training...")
-
-for trials in range(1, n_trials + 1):
-
-    # Reset environment and get initial observations for all agents
-    obs = env.reset()
-    for idx in range(env.get_num_agents()):
-        tmp_agent = env.agents[idx]
-        speed = (idx % 5) + 1
-        tmp_agent.speed_data["speed"] = 1 / speed
-    env_renderer.reset()
-    # Here you can also further enhance the provided observation by means of normalization
-    # See training navigation example in the baseline repository
-
-    score = 0
-    # Run episode
-    frame_step = 0
-    for step in range(500):
-        # Chose an action for each agent in the environment
-        for a in range(env.get_num_agents()):
-            action = agent.act(obs[a])
-            action_dict.update({a: action})
-
-        # Environment step which returns the observations for all agents, their corresponding
-        # reward and whether their are done
-        next_obs, all_rewards, done, _ = env.step(action_dict)
-        env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
-        try:
-            env_renderer.gl.save_image("./../rendering/flatland_2_0_frame_{:04d}.bmp".format(frame_step))
-        except:
-            print("Path not found: ./../rendering/")
-        frame_step += 1
-        # Update replay buffer and train agent
-        for a in range(env.get_num_agents()):
-            agent.step((obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a]))
-            score += all_rewards[a]
-
-        obs = next_obs.copy()
-        if done['__all__']:
-            break
-    print('Episode Nr. {}\t Score = {}'.format(trials, score))
+
+print("Start episode...")
+# Reset environment and get initial observations for all agents
+obs = env.reset()
+# Update/Set agent's speed
+for idx in range(env.get_num_agents()):
+    speed = 1.0 / ((idx % 5) + 1.0)
+    env.agents[idx].speed_data["speed"] = speed
+
+# Reset the rendering sytem
+env_renderer.reset()
+
+# Here you can also further enhance the provided observation by means of normalization
+# See training navigation example in the baseline repository
+
+score = 0
+# Run episode
+frame_step = 0
+for step in range(500):
+    # Chose an action for each agent in the environment
+    for a in range(env.get_num_agents()):
+        action = agent.act(obs[a])
+        action_dict.update({a: action})
+
+    # Environment step which returns the observations for all agents, their corresponding
+    # reward and whether their are done
+    next_obs, all_rewards, done, _ = env.step(action_dict)
+    env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
+    frame_step += 1
+    # Update replay buffer and train agent
+    for a in range(env.get_num_agents()):
+        agent.step((obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a]))
+        score += all_rewards[a]
+
+    obs = next_obs.copy()
+    if done['__all__']:
+        break
+
+print('Episode: Steps {}\t Score = {}'.format(step, score))