Use random actions if more than 100 agents

run.py

@@ -25,6 +25,13 @@ def infer_action(obs):
     return policy.act(norm_obs, eps=0.0)
 
 
+def random_controller(obs, number_of_agents):
+    _action = {}
+    for _idx in range(number_of_agents):
+        _action[_idx] = np.random.randint(0, 5)
+    return _action
+
+
 def rl_controller(obs, number_of_agents):
     obs_list = []
     for agent in range(number_of_agents):
@@ -52,7 +59,7 @@ if __name__ == "__main__":
     # Observation parameters
     observation_tree_depth = 1
     observation_radius = 10
-    observation_max_path_depth = 30
+    observation_max_path_depth = 20
 
     # Observation builder
     predictor = ShortestPathPredictorForRailEnv(observation_max_path_depth)
@@ -116,29 +123,35 @@ if __name__ == "__main__":
             #####################################################################
             # Evaluation of a single episode
             #####################################################################
-            time_start = time.time()
-            action = rl_controller(observation, number_of_agents)
-            agent_time = time.time() - time_start
-            time_taken_by_controller.append(agent_time)
-
-            time_start = time.time()
-            _, all_rewards, done, info = remote_client.env_step(action)
-            steps += 1
-            step_time = time.time() - time_start
-            time_taken_per_step.append(step_time)
-
-            time_start = time.time()
-            tree_observation.prepare_get_many(list(range(number_of_agents)))
-            prepare_time = time.time() - time_start
-
-            time_start = time.time()
-            observation_list = []
-            for h in range(number_of_agents):
-                observation_list.append(tree_observation.get(h))
-            observation = dict(zip(range(number_of_agents), observation_list))
-            obs_time = time.time() - time_start
-
-            # print("Step {}\t Prepare time {:.3f}\t Obs time {:.3f}\t Inference time {:.3f}\t Step time {:.3f}".format(str(steps).zfill(3), prepare_time, obs_time, agent_time, step_time))
+            if number_of_agents < 100:
+                time_start = time.time()
+                action = rl_controller(observation, number_of_agents)
+                agent_time = time.time() - time_start
+                time_taken_by_controller.append(agent_time)
+
+                time_start = time.time()
+                _, all_rewards, done, info = remote_client.env_step(action)
+                steps += 1
+                step_time = time.time() - time_start
+                time_taken_per_step.append(step_time)
+
+                time_start = time.time()
+                tree_observation.prepare_get_many(list(range(number_of_agents)))
+                prepare_time = time.time() - time_start
+
+                time_start = time.time()
+                observation_list = []
+                for h in range(number_of_agents):
+                    observation_list.append(tree_observation.get(h))
+                observation = dict(zip(range(number_of_agents), observation_list))
+                obs_time = time.time() - time_start
+
+            else:
+                # too many agents: just act randomly
+                action = random_controller(observation, number_of_agents)
+                _, all_rewards, done, info = remote_client.env_step(action)
+
+            #print("Step {}\t Predictor time {:.3f}\t Obs time {:.3f}\t Inference time {:.3f}\t Step time {:.3f}".format(str(steps).zfill(3), prepare_time, obs_time, agent_time, step_time))
 
             # if check_if_all_blocked(local_env):
             #     print("DEADLOCKED!!")