Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
LunarLander-v2
Eval Results (legacy)
Instructions to use TSunm/ppo-LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use TSunm/ppo-LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="TSunm/ppo-LunarLander-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from TSunm/ppo-LunarLander-v3: direct link, hf CLI and curl.
- Browser
- Download file 777 Bytes
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https://huggingface.co/TSunm/ppo-LunarLander-v3/resolve/main/README.md
- Command line
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hf download hf://TSunm/ppo-LunarLander-v3/README.md
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curl -L -o README.md https://huggingface.co/TSunm/ppo-LunarLander-v3/resolve/main/README.md
777 Bytes
| library_name: stable-baselines3 | |
| tags: | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| - LunarLander-v2 | |
| model-index: | |
| - name: PPO LunarLander-v2 | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: LunarLander-v2 | |
| type: LunarLander-v2 | |
| metrics: | |
| - type: mean_reward | |
| value: 269.48 +/- 16.00 | |
| name: mean_reward | |
| # **PPO** Agent playing **LunarLander-v3** | |
| This is a trained model of a **PPO** agent playing **LunarLander-v3** | |
| using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). | |
| ## Usage (with Stable-baselines3) | |
| TODO: Add your code | |
| ```python | |
| from stable_baselines3 import ... | |
| from huggingface_sb3 import load_from_hub | |
| ... | |
| ``` | |