Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Preethi0205/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Preethi0205/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Preethi0205/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library for Unit 1 of the Hugging Face Deep Reinforcement Learning Course.
Evaluation Results
- Mean Reward: 265.40 +/- 18.20
- Threshold Required: >= 200
- Pass Status: Passed ✅
Hyperparameters
n_steps: 2048
batch_size: 64
gamma: 0.999
learning_rate: 0.0003
ent_coef: 0.01
clip_range: 0.2
n_epochs: 10
gae_lambda: 0.98
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Evaluation results
- mean_reward on LunarLander-v2self-reported265.40 +/- 18.20