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README.md CHANGED
@@ -1,11 +1,10 @@
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  ---
 
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  tags:
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  - LunarLander-v2
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- - ppo
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  - deep-reinforcement-learning
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  - reinforcement-learning
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- - custom-implementation
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- - deep-rl-course
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  model-index:
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  - name: PPO
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  results:
@@ -17,45 +16,22 @@ model-index:
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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- value: -46.82 +/- 64.12
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  name: mean_reward
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  verified: false
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  ---
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- # PPO Agent Playing LunarLander-v2
 
 
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- This is a trained model of a PPO agent playing LunarLander-v2.
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-
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- # Hyperparameters
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- ```python
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- {'exp_name': 'ppo'
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- 'seed': 1
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- 'torch_deterministic': True
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- 'cuda': True
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- 'track': False
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- 'wandb_project_name': 'cleanRL'
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- 'wandb_entity': None
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- 'capture_video': False
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- 'env_id': 'LunarLander-v2'
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- 'total_timesteps': 500000
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- 'learning_rate': 0.00025
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- 'num_envs': 4
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- 'num_steps': 128
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- 'anneal_lr': True
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- 'gae': True
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- 'gamma': 0.99
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- 'gae_lambda': 0.95
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- 'num_minibatches': 4
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- 'update_epochs': 4
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- 'norm_adv': True
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- 'clip_coef': 0.2
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- 'clip_vloss': True
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- 'ent_coef': 0.01
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- 'vf_coef': 0.5
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- 'max_grad_norm': 0.5
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- 'target_kl': None
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- 'repo_id': 'hui168/LunarLander-v2'
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- 'batch_size': 512
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- 'minibatch_size': 128}
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- ```
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-
 
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  ---
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+ library_name: stable-baselines3
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  tags:
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  - LunarLander-v2
 
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  - deep-reinforcement-learning
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  - reinforcement-learning
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+ - stable-baselines3
 
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  model-index:
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  - name: PPO
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  results:
 
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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+ value: 233.00 +/- 50.65
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  name: mean_reward
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  verified: false
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  ---
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+ # **PPO** Agent playing **LunarLander-v2**
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+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
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+
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+ ```python
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+ from stable_baselines3 import ...
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+ from huggingface_sb3 import load_from_hub
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+
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+ ...
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
config.json ADDED
@@ -0,0 +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 0x7dd765ffd240>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dd765ffd2d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dd765ffd360>", 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