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model-index: - name: stable-baselines3-ppo-LunarLander-v2

ARCHIVED MODEL, DO NOT USE IT

stable-baselines3-ppo-LunarLander-v2 πŸš€πŸ‘©β€πŸš€

This is a saved model of a PPO agent playing LunarLander-v2. The model is taken from rl-baselines3-zoo

The goal is to correctly land the lander by controlling firing engines (fire left orientation engine, fire main engine and fire right orientation engine).

πŸ‘‰ You can watch the agent playing by using this notebook

Use the Model

Install the dependencies

You need to use the Stable Baselines 3 Hugging Face version of the library (this version contains the function to load saved models directly from the Hugging Face Hub):

pip install git+https://github.com/simoninithomas/stable-baselines3.git

Evaluate the agent

⚠️You need to have Linux or MacOS to be able to use this environment. If it's not the case you can use the colab notebook

# Import the libraries
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Load the environment
env = gym.make('LunarLander-v2')

model = PPO.load_from_huggingface(hf_model_id="ThomasSimonini/stable-baselines3-ppo-LunarLander-v2",hf_model_filename="LunarLander-v2")
 
# 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()

Results

Mean Reward (10 evaluation episodes): 245.63 +/- 10.02

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