==============================================================================...

====================================================================================================
## Server Performance Stats
====================================================================================================
         - message_queue_latency         => min: 0.00038123130798339844 || mean: 0.0015168707755788499 || max: 1.0546770095825195
         - current_episode_controller_inference_time     => min: 0.0006983280181884766 || mean: 0.03446188241071793 || max: 0.4753117561340332
         - controller_inference_time     => min: 0.0006983280181884766 || mean: 0.03446188241071793 || max: 0.4753117561340332
         - internal_env_step_time        => min: 0.00036644935607910156 || mean: 0.008951559738510565 || max: 0.10788345336914062
====================================================================================================
####################################################################################################
EVALUATION COMPLETE !!
####################################################################################################
# Mean Reward : -5167.23
# Sum Normalized Reward : 20.058875800849986 (primary score)
# Mean Percentage Complete : 0.753 (secondary score)
# Mean Normalized Reward : 0.66863
####################################################################################################
####################################################################################################

reinforcement_learning/multi_agent_training.py

@@ -172,7 +172,8 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
     completion_window = deque(maxlen=checkpoint_interval)
 
     # Double Dueling DQN policy
-    USE_SINGLE_AGENT_TRAINING = False
+    USE_SINGLE_AGENT_TRAINING = True
+    UPDATE_POLICY2_N_EPISODE = 1000
     policy = DDDQNPolicy(state_size, action_size, train_params)
     # policy = PPOAgent(state_size, action_size, n_agents)
     # Load existing policy
@@ -227,7 +228,7 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
         obs, info = train_env.reset(regenerate_rail=True, regenerate_schedule=True)
         policy.reset()
 
-        if episode_idx % 100 == 0:
+        if episode_idx % UPDATE_POLICY2_N_EPISODE == 0:
             policy2 = policy.clone()
 
         reset_timer.end()
@@ -499,14 +500,14 @@ def eval_policy(env, tree_observation, policy, train_params, obs_params):
 
 if __name__ == "__main__":
     parser = ArgumentParser()
-    parser.add_argument("-n", "--n_episodes", help="number of episodes to run", default=5400, type=int)
+    parser.add_argument("-n", "--n_episodes", help="number of episodes to run", default=54000, type=int)
     parser.add_argument("-t", "--training_env_config", help="training config id (eg 0 for Test_0)", default=2,
                         type=int)
-    parser.add_argument("-e", "--evaluation_env_config", help="evaluation config id (eg 0 for Test_0)", default=2,
+    parser.add_argument("-e", "--evaluation_env_config", help="evaluation config id (eg 0 for Test_0)", default=1,
                         type=int)
-    parser.add_argument("--n_evaluation_episodes", help="number of evaluation episodes", default=25, type=int)
+    parser.add_argument("--n_evaluation_episodes", help="number of evaluation episodes", default=2, type=int)
     parser.add_argument("--checkpoint_interval", help="checkpoint interval", default=100, type=int)
-    parser.add_argument("--eps_start", help="max exploration", default=1.0, type=float)
+    parser.add_argument("--eps_start", help="max exploration", default=0.1, type=float)
     parser.add_argument("--eps_end", help="min exploration", default=0.01, type=float)
     parser.add_argument("--eps_decay", help="exploration decay", default=0.9975, type=float)
     parser.add_argument("--buffer_size", help="replay buffer size", default=int(1e7), type=int)
@@ -563,6 +564,17 @@ if __name__ == "__main__":
             "malfunction_rate": 1 / 200,
             "seed": 0
         },
+        {
+            # Test_3
+            "n_agents": 58,
+            "x_dim": 40,
+            "y_dim": 40,
+            "n_cities": 5,
+            "max_rails_between_cities": 2,
+            "max_rails_in_city": 3,
+            "malfunction_rate": 1 / 200,
+            "seed": 0
+        },
     ]
 
     obs_params = {

run.py

@@ -26,7 +26,10 @@ from reinforcement_learning.dddqn_policy import DDDQNPolicy
 VERBOSE = True
 
 # Checkpoint to use (remember to push it!)
-checkpoint = "./checkpoints/201112143850-4100.pth" # 21.543589381053096 DEPTH=2
+checkpoint = "./checkpoints/201112143850-5400.pth" # 21.220418678677177 DEPTH=2 AGENTS=10
+checkpoint = "./checkpoints/201113070245-5400.pth" # 19.690047767961005 DEPTH=2 AGENTS=20
+checkpoint = "./checkpoints/201113211844-6100.pth" # 19.690047767961005 DEPTH=2 AGENTS=20
+
 
 # Use last action cache
 USE_ACTION_CACHE = False