README.md

#@title

tags: - deep-reinforcement-learning - reinforcement-learning - stable-baselines3

PPO LunarLander-v2 🚀🌑

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

Usage (with Stable-baselines3)

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

pip install stable-baselines3
pip install huggingface_sb3

Then, you can use the model like this:

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 = mrm8488/ppo-LunarLander-v2)
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="mrm8488/ppo-LunarLander-v2", filename="ppo-LunarLander-v2")
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 = env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = env.step(action)
    env.render()
    if done:
        obs = env.reset()
env.close()

Evaluation Results

Mean_reward: 254.72 +/- 21.70