Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use pujithakolipakula/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use pujithakolipakula/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="pujithakolipakula/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 trained by pujithakolipakula as part of the Hugging Face Deep RL Course.
Evaluation Results
- Mean Reward: 265.40 +/- 18.20 (Result = 247.20 >= 200.00)
Usage (with Stable-baselines3)
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
repo_id = "pujithakolipakula/ppo-LunarLander-v2"
model = load_from_hub(repo_id=repo_id, filename="ppo-LunarLander-v2.zip")
- Downloads last month
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Evaluation results
- mean_reward on LunarLander-v2self-reported265.40 +/- 18.20