salc2/rl-lunar-lander-v3-1Msteps

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 huggingface_sb3 import load_from_hub
from stable_baselines3.common.evaluation import evaluate_policy

repo_id = "salc2/rl-lunar-lander-v3-1Msteps" # The repo_id
filename = "models/ppo_lunar_steps1_M.zip"  # The model filename.zip

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint)

eval_env = gym.make("LunarLander-v3")
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
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