Reinforcement Learning
stable-baselines3
ppo
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use jcps88/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use jcps88/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="jcps88/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 Stable-Baselines3, achieving a mean reward of 213.9.
Usage
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
# Load model
checkpoint = load_from_hub(
repo_id="jcps88/ppo-LunarLander-v2",
filename="best_model.zip",
)
model = PPO.load(checkpoint)
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Evaluation results
- mean_reward on LunarLander-v2self-reported213.9 +/- 0.0