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---
tags:
- Pong-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: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 4.50 +/- 3.93
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-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/DQPN_p100_pt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p100_pt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p100_pt0.1 --env-id Pong-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/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p100_pt0.1 --start-policy-f 100000 --end-policy-f 100000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 100000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p100_pt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 0.1,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 100000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
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