---
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: 262.43 +/- 18.65
      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)
TODO: Add your code


```python
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
import gym

# Define model repo_id and filename
repo_id = "mavleo96/rl-bots"  # Change this to the actual repo if different
filename = "ppo-LunarLander-v2.zip"

# Load the model from the Hugging Face Hub
model = load_from_hub(repo_id, filename, model_class=PPO)

# Create the environment
env = gym.make("LunarLander-v2")

# Run a few episodes
obs = env.reset()
for _ in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()
    if done:
        obs = env.reset()

env.close()
```