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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 241.85 +/- 48.02
name: mean_reward
verified: false
---
# **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](https://github.com/DLR-RM/stable-baselines3).
## 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}")
``` |