ppo-LunarLander-v3 Agent playing LunarLander-v3

This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library and gymnasium.

Evaluation

metric	value
mean_reward	268.51
std_reward	13.07
mean - std	255.44
episodes	10

Usage (with Stable-Baselines3)

import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

repo_id = "ppo-LunarLander-v3"          # or <your-user>/<repo-name>
filename = "ppo-LunarLander-v3.zip"

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint)
env = gym.make("LunarLander-v3", render_mode="rgb_array")
obs, _ = env.reset()
for _ in range(1000):
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, _ = env.reset()
env.close()

Files

• ppo-LunarLander-v3.zip - the trained policy
• replay.mp4 - a deterministic replay of one episode
• results.json - raw evaluation numbers
• config.json - the constructor arguments stored inside the checkpoint