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Upload PPO LunarLander-v2 trained agent

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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
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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: 245.46 +/- 68.97
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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** 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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+
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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 0x7f6c416b8dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6c416b8e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6c416b8ee0>", 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+ {
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+ "policy_class": {
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+ ":type:": "<class 'abc.ABCMeta'>",
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+ "__module__": "stable_baselines3.common.policies",
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+ "__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 ",
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+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7f6c416b8dc0>",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6c416b8e50>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6c416b8ee0>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6c416b8f70>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7f6c416b9000>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7f6c416b9090>",
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+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6c416b9120>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6c416b91b0>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7f6c416b9240>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6c416b92d0>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6c416b9360>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6c416b93f0>",
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+ "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc._abc_data object at 0x7f6c4185d4c0>"
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+ },
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+ "verbose": 1,
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+ "policy_kwargs": {},
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+ - OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
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+ - Python: 3.10.12
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+ - Stable-Baselines3: 2.0.0a5
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+ - PyTorch: 2.3.0+cu121
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+ - GPU Enabled: True
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+ - Numpy: 1.25.2
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+ - Cloudpickle: 2.2.1
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+ - Gymnasium: 0.28.1
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+ - OpenAI Gym: 0.25.2
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+ {"mean_reward": 245.46417119999995, "std_reward": 68.96655443902499, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-29T21:04:11.221003"}