Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use qmmiao/RL_playbook with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use qmmiao/RL_playbook with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="qmmiao/RL_playbook", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent Playing LunarLander-v3
This is a trained model of a PPO agent playing LunarLander-v3 using stable-baselines3.
Evaluation Results
- Mean Reward: 240.65 +/- 77.66
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Evaluation results
- mean_reward on LunarLander-v3self-reported240.65 +/- 77.66