---
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
---

# **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  
    )