Reinforcement Learning
stable-baselines3
LunarLander-v3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use sjin59/ppo-LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use sjin59/ppo-LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="sjin59/ppo-LunarLander-v3", 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 the stable-baselines3 library.
Usage (with Stable-baselines3)
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
import gymnasium as gym
repo_id = "sjin59/ppo-LunarLander-v3"
filename = "ppo-LunarLander-v3.zip"
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, print_system_info=True)
env = gym.make("LunarLander-v3", render_mode="human")
obs, info = env.reset()
for _ in range(1000):
action, _states = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
obs, info = env.reset()
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Evaluation results
- mean_reward on LunarLander-v3self-reported268.37 +/- 16.59