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 +94 -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: 284.42 +/- 21.12
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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 0x7f8bf6bbb670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8bf6bbb700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8bf6bbb790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8bf6bbb820>", "_build": "<function 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ppo-LunarLander-v2/policy.optimizer.pth
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oid sha256:3b8335dad59cd43dda4c99ab1979fb12a6bc51dbcec801abc66fc505edf4e418
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ppo-LunarLander-v2/policy.pth
ADDED
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oid sha256:7c55ded8d13a88f6fca145953770669862d585e09a96d8946ef302d6d6f5eb63
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size 43201
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ppo-LunarLander-v2/pytorch_variables.pth
ADDED
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ppo-LunarLander-v2/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: 3.8.15
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Stable-Baselines3: 1.6.2
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PyTorch: 1.12.1+cu113
|
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|
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Numpy: 1.21.6
|
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results.json
ADDED
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{"mean_reward": 284.42189610783396, "std_reward": 21.124250617473013, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-07T02:38:42.525573"}
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