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coledie/
ppo-LunarLander-v2

Name: ppo-LunarLander-v2
Availability: InStock

stable-baselines3

LunarLander-v2

deep-reinforcement-learning

reinforcement-learning

model-index

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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)

python

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}")

coledie/ppo-LunarLander-v2

PPO Agent playing LunarLander-v2

Usage (with Stable-baselines3)

coledie/
ppo-LunarLander-v2