Upload PPO LunarLander-v2 trained agent with basic params

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: -122.83 +/- 56.60
+      value: 249.33 +/- 45.08
       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.
+## Usage (with Stable-baselines3)
+TODO: Add your code
 
-  # 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': 1000000
-'learning_rate': 0.00025
-'num_envs': 16
-'num_steps': 1024
-'anneal_lr': True
-'gae': True
-'gamma': 0.999
-'gae_lambda': 0.98
-'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': 'jfelgate/ppo-LunarLander-v2'
-'batch_size': 16384
-'minibatch_size': 4096}
-  ```
-  
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -1 +1 @@
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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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7daac08abb50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7daac08abbe0>", "reset_noise": 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ppo-LunarLander-v2.zip

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     "max_grad_norm": 0.5,
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-    "n_epochs": 8,
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.1.0+cu118
-- GPU Enabled: False
+- GPU Enabled: True
 - Numpy: 1.23.5
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1

results.json

@@ -1 +1 @@
-{"env_id": "LunarLander-v2", "mean_reward": -122.83178432650357, "std_reward": 56.59713764776958, "n_evaluation_episodes": 10, "eval_datetime": "2023-11-05T13:49:36.374630"}
+{"mean_reward": 249.3274329778937, "std_reward": 45.08295860333011, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-06T04:14:23.820848"}