Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Atharva1232/PPO-LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Atharva1232/PPO-LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Atharva1232/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)
TODO: Add your code
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
import gymnasium as gym
checkpoint = load_from_hub(
repo_id="Atharva1232/PPO-LunarLander-v3",
filename="ppo-LunarLander-v3.zip",
)
model = PPO.load(checkpoint)
env = gym.make("LunarLander-v3", render_mode="human")
obs, info = env.reset()
while True:
action, _ = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
obs, info = env.reset()
- Downloads last month
- 8
Evaluation results
- mean_reward on LunarLander-v3self-reported256.28 +/- 16.67