README.md

metadata

library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - metrics:
          - type: mean_reward
            value: 245.74 +/- 18.06
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2

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
from stable_baselines3.common.env_util import make_vec_env

env = make_vec_env('LunarLander-v2', n_envs=16)
model = PPO('MlpPolicy', env, verbose=1)

model.learn(total_timesteps=5 * 10**5)

eval_env = gym.make('LunarLander-v2')
mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=10, deterministic=True)
print(f"Reward mean: {mean_reward:.2f}, Reward STD: {std_reward:.2f}")