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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: 255.80 +/- 42.91
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)
To use this model, you need to have `stable-baselines3` and `huggingface_sb3` installed. You can install them using pip:
```bash
pip install stable-baselines3 huggingface_sb3 gymnasium
```python
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
import gymnasium as gym
# Identifier for the repository and model file name
repo_id = "TyurinYuriRost/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
# Load the model checkpoint from Hugging Face Hub
checkpoint = load_from_hub(repo_id=repo_id, filename=filename)
# Load the PPO model
model = PPO.load(checkpoint)
# Create the environment for evaluation
env = gym.make("LunarLander-v3", render_mode="human")
obs = env.reset()
# Visualize the model's performance
for _ in range(1000):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
if dones:
obs = env.reset()
# Close the environment
env.close()
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