Updated logging to wandb

cooperative_cuisine/reinforcement_learning/config/additional_configs/additional_config_base.yaml

@@ -4,13 +4,13 @@ state_converter:
 log_path: "logs/reinforcement_learning"
 checkpoint_path: "rl_agent_checkpoints"
 render_mode: "rgb_array"
-project_name: "overcooked"
+project_name: "overcooked_rl"
 debug_mode: False
 vec_env: True
 sync_tensorboard: True  # auto-upload sb3's tensorboard metrics
 monitor_gym: True
 video_save_path: "logs/reinforcement_learning/videos/"
-record_video_trigger: 200_000
+record_video_trigger: 20_000
 video_length: 300
 save_freq: 50_000
 save_path_callback: "logs"

cooperative_cuisine/reinforcement_learning/train_single_agent.py

@@ -28,10 +28,12 @@ def main(cfg: DictConfig):
     rl_agent_checkpoints: Path = rl_logs / Path(additional_configs["checkpoint_path"])
     rl_agent_checkpoints.mkdir(exist_ok=True)
     config: dict[str, Any] = OmegaConf.to_container(cfg.model, resolve=True)
+    env_info: dict[str, Any] = OmegaConf.to_container(cfg.environment, resolve=True)
     debug: bool = additional_configs["debug_mode"]
     vec_env = additional_configs["vec_env"]
     number_envs_parallel = config["number_envs_parallel"]
     model_class = instantiate(cfg.model.model_type)
+    data_to_log=dict(config, **env_info)
     if vec_env:
         env = make_vec_env(lambda: EnvGymWrapper(cfg), n_envs=number_envs_parallel)
     else: