---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: CartPole-v1
      type: CartPole-v1
    metrics:
    - type: mean_reward
      value: 403.02 +/- 0.00
      name: mean_reward
      verified: false
---

# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**

This is a trained model of a DQPN_freq 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/DQPN_freq_200_0.99.py).

## Get Started

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

```
pip install "cleanrl[DQPN_freq_200_0.99]"
python -m cleanrl_utils.enjoy --exp-name DQPN_freq_200_0.99 --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/pfunk/CartPole-v1-DQPN_freq_200_0.99-seed2/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_freq_200_0.99-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_freq_200_0.99-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQPN_freq_200_0.99 --gamma 0.99 --policy-network-frequency 200 --seed 2
```

# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
 'batch_size': 256,
 'buffer_size': 300000,
 'capture_video': True,
 'cuda': True,
 'end_e': 0.1,
 'env_id': 'CartPole-v1',
 'exp_name': 'DQPN_freq_200_0.99',
 'exploration_fraction': 0.2,
 'gamma': 0.99,
 'hf_entity': 'pfunk',
 'learning_rate': 0.0001,
 'learning_starts': 1000,
 'policy_network_frequency': 200,
 'policy_tau': 1.0,
 'save_model': True,
 'seed': 2,
 'start_e': 1.0,
 'target_network_frequency': 20,
 'target_tau': 1.0,
 'torch_deterministic': True,
 'total_timesteps': 500000,
 'track': True,
 'train_frequency': 1,
 'upload_model': True,
 'wandb_entity': 'pfunk',
 'wandb_project_name': 'dqpn'}
```