Auto-set num_envs_per_worker in rllib_example.py

examples/rllib_example.py

@@ -49,13 +49,21 @@ def env_creator(env_config):
 
     tune.register_env(env_name, env_creator)
 
-    # Make temp env to get info needed for multi-agent training config
-    if is_multiagent:
+    policy_names = None
+    num_envs = None
+    tmp_env = None
+
+    if is_multiagent:  # Make temp env to get info needed for multi-agent training config
         print("Starting a temporary multi-agent env to get the policy names")
         tmp_env = GDRLPettingZooEnv(config=exp["config"]["env_config"], show_window=False)
         policy_names = tmp_env.agent_policy_names
         print("Policy names for each Agent (AIController) set in the Godot Environment", policy_names)
-        tmp_env.close()
+    else:  # Make temp env to get info needed for setting num_workers training config
+        print("Starting a temporary env to get the number of envs and auto-set the num_envs_per_worker config value")
+        tmp_env = GodotEnv(env_path=exp["config"]["env_config"]["env_path"], show_window=False)
+        num_envs = tmp_env.num_envs
+
+    tmp_env.close()
 
     def policy_mapping_fn(agent_id: int, episode, worker, **kwargs) -> str:
         return policy_names[agent_id]
@@ -67,6 +75,8 @@ def policy_mapping_fn(agent_id: int, episode, worker, **kwargs) -> str:
             "policies": {policy_name: PolicySpec() for policy_name in policy_names},
             "policy_mapping_fn": policy_mapping_fn,
         }
+    else:
+        exp["config"]["num_envs_per_worker"] = num_envs
 
     tuner = None
     if not args.restore:
@@ -89,6 +99,7 @@ def policy_mapping_fn(agent_id: int, episode, worker, **kwargs) -> str:
 
     # Onnx export after training if a checkpoint was saved
     checkpoint = result.get_best_result().checkpoint
+
     if checkpoint:
         result_path = result.get_best_result().path
         ppo = Algorithm.from_checkpoint(checkpoint)