Upload PPO LunarLander-v2 trained agent

README.md

@@ -1,11 +1,10 @@
 ---
+library_name: stable-baselines3
 tags:
 - LunarLander-v2
-- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- custom-implementation
-- deep-rl-course
+- stable-baselines3
 model-index:
 - name: PPO
   results:
@@ -17,45 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -13.78 +/- 46.16
+      value: 295.33 +/- 16.59
       name: mean_reward
       verified: false
 ---
 
-    # PPO Agent Playing LunarLander-v2
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
-    This is a trained model of a PPO agent playing LunarLander-v2.
-        
-    # Hyperparameters
-    ```python
-    {'exp_name': 'ppo'
-'seed': 1
-'torch_deterministic': True
-'cuda': True
-'track': False
-'wandb_project_name': 'cleanRL'
-'wandb_entity': None
-'capture_video': False
-'env_id': 'LunarLander-v2'
-'total_timesteps': 500000
-'learning_rate': 0.00025
-'num_envs': 4
-'num_steps': 512
-'anneal_lr': True
-'gae': True
-'gamma': 0.999
-'gae_lambda': 0.95
-'num_minibatches': 4
-'update_epochs': 4
-'norm_adv': True
-'clip_coef': 0.2
-'clip_vloss': True
-'ent_coef': 0.01
-'vf_coef': 0.5
-'max_grad_norm': 0.5
-'target_kl': None
-'repo_id': 'utyug1/ppo-LunarLander-v2'
-'batch_size': 2048
-'minibatch_size': 512}
-    ```
-    
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -1 +1 @@
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If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f973a541310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f973a5413a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f973a541430>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"env_id": "LunarLander-v2", "mean_reward": -13.781872383860962, "std_reward": 46.15839622555713, "n_evaluation_episodes": 10, "eval_datetime": "2023-04-08T22:00:54.492751"}
+{"mean_reward": 295.33036897216004, "std_reward": 16.589061756996717, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-08T22:33:48.426490"}