Instructions to use marvameraj/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use marvameraj/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="marvameraj/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from marvameraj/ppo-LunarLander-v2: direct link, hf CLI and curl.
- Browser
- Download file 636 Bytes
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https://huggingface.co/marvameraj/ppo-LunarLander-v2/resolve/main/README.md
- Command line
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hf download hf://marvameraj/ppo-LunarLander-v2/README.md
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curl -L -o README.md https://huggingface.co/marvameraj/ppo-LunarLander-v2/resolve/main/README.md
636 Bytes
| tags: | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| - LunarLander-v2 | |
| - deep-rl-course | |
| 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: "269.79 +/- 20.99" | |
| name: mean_reward | |
| # PPO LunarLander-v2 | |
| Deep Reinforcement Learning Course — Unit 1. | |
| ## Evaluation | |
| Environment: `LunarLander-v2` | |
| Mean reward: `269.79` | |
| Standard deviation: `20.99` | |
| Course score: `248.80` | |
| Evaluation episodes: `20` | |
| Training timesteps: `1,000,000` | |