first trained agent with proximal policy optimization
Browse files- README.md +37 -0
- config.json +1 -0
- lunar_lander_ppo.zip +3 -0
- lunar_lander_ppo/_stable_baselines3_version +1 -0
- lunar_lander_ppo/data +91 -0
- lunar_lander_ppo/policy.optimizer.pth +3 -0
- lunar_lander_ppo/policy.pth +3 -0
- lunar_lander_ppo/pytorch_variables.pth +3 -0
- lunar_lander_ppo/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: 286.12 +/- 17.95
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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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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 0x7f705cdad820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f705cdad8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f705cdad940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f705cdad9d0>", "_build": "<function 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lunar_lander_ppo/policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:d4d546aaa07b153ba2b6f65cbc09e2bc3481cb1089519e64cf0551fd7ad9bd64
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size 88057
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lunar_lander_ppo/policy.pth
ADDED
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lunar_lander_ppo/pytorch_variables.pth
ADDED
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lunar_lander_ppo/system_info.txt
ADDED
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OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
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Python: 3.8.16
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Stable-Baselines3: 1.6.2
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PyTorch: 1.13.0+cu116
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|
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|
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results.json
ADDED
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{"mean_reward": 286.12453207862944, "std_reward": 17.950547966482564, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-12T02:05:07.148364"}
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