README.md

---
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: BreakoutNoFrameskip-v4
      type: BreakoutNoFrameskip-v4
    metrics:
    - type: mean_reward
      value: 221.40 +/- 32.78
      name: mean_reward
      verified: false
---

# (CleanRL) **DQN** Agent Playing **BreakoutNoFrameskip-v4**

This is a trained model of a DQN agent playing BreakoutNoFrameskip-v4.
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_atari.py).

## Get Started

To use this model, please install the `cleanrl` package with the following command:

```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id BreakoutNoFrameskip-v4
```

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/odiaz1066/BreakoutNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/odiaz1066/BreakoutNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/odiaz1066/BreakoutNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --track --save-model --capture-video --resume --total-timesteps 0 --upload-model --seed 1 --hf-entity odiaz1066
```

# Hyperparameters
```python
{'batch_size': 32,
 'buffer_size': 1000000,
 'capture_video': True,
 'checkpoint': False,
 'checkpoint_frequency': 100000,
 'cuda': True,
 'end_e': 0.01,
 'env_id': 'BreakoutNoFrameskip-v4',
 'exp_name': 'dqn_atari',
 'exploration_fraction': 0.1,
 'gamma': 0.99,
 'hf_entity': 'odiaz1066',
 'initial_steps': 0,
 'learning_rate': 0.0001,
 'learning_starts': 80000,
 'num_envs': 1,
 'resume': True,
 'rotate': False,
 'save_model': True,
 'seed': 1,
 'start_e': 1,
 'target_network_frequency': 1000,
 'tau': 1.0,
 'torch_deterministic': True,
 'total_timesteps': 0,
 'track': True,
 'train_frequency': 4,
 'upload_model': True,
 'wandb_entity': None,
 'wandb_project_name': 'lagomorph'}
```