Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Guru-Raja-124/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Guru-Raja-124/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Guru-Raja-124/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO LunarLander-v2
Stable-Baselines3 PPO agent trained on LunarLander-v2.
Evaluation
- Mean reward: 255.26
- Standard deviation: 22.00
- Certification score (
mean_reward - std_reward): 233.25 - Evaluation episodes: 10
- Training timesteps: 1,000,000
Training
This model was trained using Stable-Baselines3 PPO with an MLP policy.
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Evaluation results
- mean_reward on LunarLander-v2self-reported255.26 +/- 22.00