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Upload PPO LunarLander-v2 trained agent

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  1. README.md +16 -40
  2. config.json +1 -1
  3. replay.mp4 +0 -0
  4. results.json +1 -1
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: -13.78 +/- 46.16
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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': 512
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- 'anneal_lr': True
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- 'gae': True
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- 'gamma': 0.999
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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': 'utyug1/ppo-LunarLander-v2'
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- 'batch_size': 2048
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- 'minibatch_size': 512}
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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: 295.33 +/- 16.59
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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 CHANGED
@@ -1 +1 @@
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- {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. 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 ActorCriticPolicy._build_mlp_extractor at 0x7f973a5414c0>", "_build": "<function 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