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---
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
)
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