ChihoonLee3/ppomlppolicy-LunarLander-v2

PPO MlpPolicy Agent playing LunarLander-v2

This is a trained model of a PPO MlpPolicy agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

TODO: Add your code

import gymnasium as gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.evaluation import evaluate_policy


repo_id = "ChihoonLee3/ppomlppolicy-LunarLander-v2"  # The repo_id
filename = "ppo-LunarLander-v2.zip"  # The model filename.zip

# Load the PPO model
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, print_system_info=True)

# Evaluate the model in LunarLander-v2 environment
eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")