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 +99 -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 +9 -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: 206.08 +/- 85.87
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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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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 0x7fd218461090>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd218461120>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd2184611b0>", 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ppo-LunarLander-v2/policy.optimizer.pth
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size 87978
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ppo-LunarLander-v2/policy.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e673888a34a62771140f3e4a7d17f11b7cd0f77995a3cd003896ad9f75481f21
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size 43634
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ppo-LunarLander-v2/pytorch_variables.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
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size 864
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ppo-LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,9 @@
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1 |
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- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:18:04 UTC 2024
|
2 |
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- Python: 3.10.12
|
3 |
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- Stable-Baselines3: 2.0.0a5
|
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- PyTorch: 2.3.0+cu121
|
5 |
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- GPU Enabled: False
|
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- Numpy: 1.25.2
|
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- Cloudpickle: 2.2.1
|
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- Gymnasium: 0.28.1
|
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- OpenAI Gym: 0.25.2
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replay.mp4
ADDED
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results.json
ADDED
@@ -0,0 +1 @@
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{"mean_reward": 206.07823630000001, "std_reward": 85.87454475761106, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-28T06:02:58.181193"}
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