ThomasSimonini
HF staff
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Commit
cbc7a0c
1 Parent(s): 75ba32c

Test commit

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.gitattributes CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - Walker2DBulletEnv-v0
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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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+ - metrics:
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+ - type: mean_reward
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+ value: 19.51 +/- 1.95
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+ name: mean_reward
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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: Walker2DBulletEnv-v0
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+ type: Walker2DBulletEnv-v0
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+ ---
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+
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+ # **PPO** Agent playing **Walker2DBulletEnv-v0**
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+ This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0** 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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+
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 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 0x7f898514c560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f898514c5f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f898514c680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f898514c710>", "_build": "<function 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