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---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 438.60 +/- 87.15
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn]"
python -m cleanrl_utils.enjoy --exp-name dqn --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co./nsanghi/CartPole-v1-dqn-seed1/raw/main/dqn.py
curl -OL https://huggingface.co./nsanghi/CartPole-v1-dqn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co./nsanghi/CartPole-v1-dqn-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --seed 1 --env-id CartPole-v1 --total-timesteps 50000 --track --capture-video --save-model --upload-model --hf-entity nsanghi
```
# Hyperparameters
```python
{'batch_size': 128,
'buffer_size': 10000,
'capture_video': True,
'cuda': True,
'end_e': 0.05,
'env_id': 'CartPole-v1',
'exp_name': 'dqn',
'exploration_fraction': 0.5,
'gamma': 0.99,
'hf_entity': 'nsanghi',
'learning_rate': 0.00025,
'learning_starts': 10000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 500,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 50000,
'track': True,
'train_frequency': 10,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
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