---
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: 294.83 +/- 15.86
      name: mean_reward
      verified: false
license: mit
---

# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)
TODO: Add to your code


```python
import gymnasium as gym

from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

checkpoint_model = load_from_hub(
    repo_id="Creador270/ppo-LunarLander-v2_hello_RL",
    filename="ppo-LunarLander-v2_hello_RL_2.zip",

model = PPO.load(checkpoint_model) #The model you will be using

env = gym.make("LunarLander-v2")

observation, info = env.reset()

#It must be deterministic because, in the action space, we can only choose between which motor must be activated
action, _states = model.predict(observation, deterministic=True)
)
```