README.md

---
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}")

```