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 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="epsil/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()
Created by Saurabh Mishra
Made with ๐ in India
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Evaluation results
- mean_reward on LunarLander-v2self-reported124.30 +/- 74.63