PPO LunarLander V2

🚀 Stable-Baselines3 PPO Agent for LunarLander-v2

This project presents a trained PPO agent for the LunarLander-v2 environment using the stable-baselines3 library, offering a practical solution for deep reinforcement learning.

🚀 Quick Start

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

📦 Installation

Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:

pip install stable-baselines3
pip install huggingface_sb3

💻 Usage Examples

Basic Usage

import gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Retrieve the model from the hub
## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="BioGeek/PPO-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
model = PPO.load(checkpoint)

# Evaluate the agent
eval_env = 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}")
 
# Watch the agent play
obs = eval_env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = eval_env.step(action)
    eval_env.render()
    if done:
        obs = eval_env.reset()
eval_env.close()

📚 Documentation