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, and huggingface_sb3)
To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions of Python on your system.
Install required packages:
pip install stable-baselines3
pip install huggingface_sb3
pip install pickle5
pip install Box2D
pip install pyglet
You can use this simple script as a base to evaluate and run the model:
import gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from stable_baselines3.common.evaluation import evaluate_policy
# Download the model from the huggingface hub
checkpoint = load_from_hub(
repo_id="kalmufti/PPO-LunarLander-v2",
filename="ppo-LunarLander-v2.zip",
)
# Load the policy
model = PPO.load(checkpoint)
# Create an environment
env = gym.make("LunarLander-v2")
# Optional - evaluate the agent means
mean_reward, std_reward = evaluate_policy(
model, env, render=False, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Watch the agent playing the environment
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()
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Evaluation results
- mean_reward on LunarLander-v2self-reported275.34 +/- 14.56