
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** 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 ...
from huggingface_sb3 import load_from_hub

#The hyper-parameter
model = DQN(
    "MlpPolicy", 
    env=env,
    buffer_size=100000,  
    learning_starts=50000,  
    batch_size=128,  
    train_freq=1,  
    gradient_steps=3,    
    tau=1.0, 
    gamma=0.99,   
    learning_rate=0.0001,  
    target_update_interval=10000,  
    exploration_initial_eps=1.0,
    exploration_fraction=0.1,  
    exploration_final_eps=0.05,  
    policy_kwargs=dict(net_arch=[256, 256]),
    device='auto',  
    verbose=1  
    )



{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "241.60 +/- 48.03", "name": "mean_reward", "verified": false}]}]}]}
stable-baselines3 LunarLander-v2 deep-reinforcement-learning reinforcement-learning model-index region:us