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unit0-ppo

Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Model card Files
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  1. README.md +37 -0
  2. config.json +1 -0
  3. ppo_mlp.zip +3 -0
  4. ppo_mlp/_stable_baselines3_version +1 -0
  5. ppo_mlp/data +99 -0
  6. ppo_mlp/policy.optimizer.pth +3 -0
  7. ppo_mlp/policy.pth +3 -0
  8. ppo_mlp/pytorch_variables.pth +3 -0
  9. ppo_mlp/system_info.txt +9 -0
  10. replay.mp4 +0 -0
  11. results.json +1 -0
README.md ADDED
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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-MlpPolicy
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: LunarLander-v2
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+ type: LunarLander-v2
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+ metrics:
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+ - type: mean_reward
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+ value: 268.08 +/- 17.81
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **PPO-MlpPolicy** Agent playing **LunarLander-v2**
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+ This is a trained model of a **PPO-MlpPolicy** agent playing **LunarLander-v2**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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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
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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 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 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 0x7f007bdd6ef0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f007bdd6f80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f007bdd7010>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f007bdd70a0>", "_build": "<function ActorCriticPolicy._build at 0x7f007bdd7130>", "forward": "<function ActorCriticPolicy.forward at 0x7f007bdd71c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f007bdd7250>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f007bdd72e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f007bdd7370>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f007bdd7400>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f007bdd7490>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f007bdd7520>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f007bf777c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, 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