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Dabe/
LunarLanderPPO2

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, trained for 1e6 time steps, obtaining: mean_reward = 241.85 +/- 48.02

using the stable-baselines3 library.

Usage (with Stable-baselines3)

python

import gym                     

from stable_baselines3 import PPO                                # Modelo que vamos a usar
from stable_baselines3.common.evaluation import evaluate_policy  # Evaluación de los resultados del modelo entrenado
from stable_baselines3.common.env_util import make_vec_env

# Creo el env
env = gym.make('LunarLander-v2')

# Selecciono el modelo, en este caso el PPO, y lo ponemos a entrenar
model = PPO('MlpPolicy',env,verbose=1).learn(total_timesteps=1000000,progress_bar=True)

# Lo guardamos
model.save('Lunar_Lander')

# Creamos un nuevo env en el que probamos el modelo (valdría el mismo pero reseteado)
eval_env = gym.make('LunarLander-v2')

# Evaluamos el modelo
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)

# Print the results
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

Dabe/LunarLanderPPO2

PPO Agent playing LunarLander-v2

Usage (with Stable-baselines3)

Dabe/
LunarLanderPPO2