(CleanRL) DQPN_freq Agent Playing PongNoFrameskip-v4
This is a trained model of a DQPN_freq agent playing PongNoFrameskip-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[P_DQPN_x3]"
python -m cleanrl_utils.enjoy --exp-name P_DQPN_x3 --env-id PongNoFrameskip-v4
Please refer to the documentation for more detail.
Command to reproduce the training
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/dqpn_freq_atari.py
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x3 --policy-network-frequency 3000 --seed 929
Hyperparameters
{'alg_type': 'dqpn_freq_atari.py',
'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'double_learning': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'P_DQPN_x3',
'exploration_fraction': 0.2,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 10000,
'max_gradient_norm': inf,
'policy_network_frequency': 3000,
'policy_tau': 1.0,
'save_model': True,
'seed': 929,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 5000000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
Evaluation results
- mean_reward on PongNoFrameskip-v4self-reported20.32 +/- 0.00