Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +96 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
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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- 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: 230.90 +/- 69.35
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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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```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:": "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 0x7f68a3d01cf0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f68a3d01d80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f68a3d01e10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f68a3d01ea0>", "_build": "<function ActorCriticPolicy._build at 0x7f68a3d01f30>", "forward": "<function ActorCriticPolicy.forward at 0x7f68a3d01fc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f68a3d02050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f68a3d020e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f68a3d02170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f68a3d02200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f68a3d02290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f68a3d02320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f68a3cfe3c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, 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ppo-LunarLander-v2/policy.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:5ed20edc7a673805daf1809394d41984bd4a5874887fa8a5122096b587aaf98a
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size 43329
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ppo-LunarLander-v2/pytorch_variables.pth
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
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size 431
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ppo-LunarLander-v2/system_info.txt
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- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
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- Python: 3.10.11
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- Stable-Baselines3: 1.8.0
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- PyTorch: 2.0.0+cu118
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- GPU Enabled: True
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- Numpy: 1.22.4
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- Gym: 0.21.0
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replay.mp4
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Binary file (215 kB). View file
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results.json
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{"mean_reward": 230.89565957730065, "std_reward": 69.35351907924611, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-27T19:48:09.894538"}
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