ArpanSriv/ppo-LunarLander-v2

PPO Agent playing LunarLander-v2

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

Usage (with Stable-baselines3)

import gymnasium as gym
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from huggingface_sb3 import load_from_hub

repo_id = "ArpanSriv/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
checkpoint = load_from_hub(repo_id, filename)

model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

env = gym.make("LunarLander-v2", render_mode="human")
observation, info = env.reset()
for _ in range(1000):
    action, _states = model.predict(observation)  # agent policy that uses the observation and info
    observation, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        observation, info = env.reset()
env.close()

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