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ppo-LunarLander-v2
coledie/ppo-LunarLander-v2
ppo-LunarLander-v2 is a reinforcement learning model from coledie. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Jul 3, 2022
- Updated
- Jul 3, 2022
- SHA
- d9426edc
Model card
From the Hugging Face model README
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}")
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Jul 3, 2022
- Updated
- Jul 3, 2022