---
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: 262.89 +/- 16.52
      name: mean_reward
      verified: false
---

# **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)

```python
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.env_util import make_vec_env
import gymnasium as gym

# Load the model from the Hub
checkpoint = load_from_hub(
    repo_id="HamzaChera/ppo-LunarLander-v2",
    filename="ppo-LunarLander-v2.zip",
)

model = PPO.load(checkpoint)

# Create the environment
env = make_vec_env("LunarLander-v2", n_envs=1)

obs = env.reset()
while True:
    action, _states = model.predict(obs, deterministic=True)
    obs, rewards, dones, info = env.step(action)
    env.render()
...
```