(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[DQPN_p50_e0.25]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p50_e0.25 --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-DQPN_p50_e0.25-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.25-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.25-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p50_e0.25 --start-policy-f 50000 --end-policy-f 1000 --evaluation-fraction 0.25 --target-tau 1.0 --policy-tau 1.00 --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
{'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': 0.25,
'exp_name': 'DQPN_p50_e0.25',
'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': 50000,
'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'}