(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 and the most up-to-date training code can be found here.

Get Started

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

pip install "cleanrl[no_tau_sweep]"
python -m cleanrl_utils.enjoy --exp-name no_tau_sweep --env-id Pong-v4

Please refer to the documentation for more detail.

Command to reproduce the training

curl -OL https://huggingface.co/pfunk/Pong-v4-no_tau_sweep-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-no_tau_sweep-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-no_tau_sweep-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --end-policy-f=1000 --env-id=Pong-v4 --evaluation-fraction=0.75 --exp-name=no_tau_sweep --hf-entity=pfunk --policy-tau=1 --save-model=true --seed=1 --start-policy-f=250000 --target-tau=1 --total-timesteps=10000000 --track=true --upload-model=true --wandb-entity=pfunk --wandb-project-name=dqpn

Hyperparameters

{'batch_size': 32,
 'buffer_size': 1000000,
 'capture_video': False,
 'cuda': True,
 'end_e': 0.01,
 'end_policy_f': 1000,
 'env_id': 'Pong-v4',
 'evaluation_fraction': 0.75,
 'exp_name': 'no_tau_sweep',
 'exploration_fraction': 0.1,
 'gamma': 0.99,
 'hf_entity': 'pfunk',
 'learning_rate': 0.0001,
 'learning_starts': 80000,
 'policy_tau': 1.0,
 'save_model': True,
 'seed': 1,
 'start_e': 1,
 'start_policy_f': 250000,
 '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'}