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- README.md +36 -0
- config.json +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- yeehaw.zip +3 -0
- yeehaw/_stable_baselines3_version +1 -0
- yeehaw/data +94 -0
- yeehaw/policy.optimizer.pth +3 -0
- yeehaw/policy.pth +3 -0
- yeehaw/pytorch_variables.pth +3 -0
- yeehaw/system_info.txt +7 -0
.gitattributes
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README.md
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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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- metrics:
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- type: mean_reward
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value: 240.31 +/- 12.46
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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: LunarLander-v2
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type: LunarLander-v2
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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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```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
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f3b9993f290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3b9993f320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3b9993f3b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3b9993f440>", "_build": "<function 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"normalize_advantage": true,
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"target_kl": null
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}
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yeehaw/policy.optimizer.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:7021951c516ab98f2567d2ae08d997a6ee9aa8ebc3e0b3f56f7d2233479ac68e
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size 84893
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yeehaw/policy.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:b616deac2285f583310276ab8cf9af34c61a7f1524ef1ac4d2f0075751626bcc
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size 43201
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yeehaw/pytorch_variables.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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yeehaw/system_info.txt
ADDED
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OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
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Python: 3.7.13
|
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Stable-Baselines3: 1.5.0
|
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PyTorch: 1.11.0+cu113
|
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GPU Enabled: True
|
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Numpy: 1.21.6
|
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Gym: 0.21.0
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