(CleanRL) DQN Agent Playing PongNoFrameskip-v4
This is a trained model of a DQN 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[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id PongNoFrameskip-v4
Please refer to the documentation for more detail.
Command to reproduce the training
curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --env-id PongNoFrameskip-v4 --track --save-model --capture-video --resume --total-timesteps 0 --upload-model --seed 1 --hf-entity odiaz1066
Hyperparameters
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'checkpoint': False,
'checkpoint_frequency': 100000,
'cuda': True,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'odiaz1066',
'initial_steps': 0,
'learning_rate': 0.0001,
'learning_starts': 80000,
'num_envs': 1,
'resume': True,
'rotate': False,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 0,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'lagomorph'}
Space using odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1 1
Evaluation results
- mean_reward on PongNoFrameskip-v4self-reported18.80 +/- 1.25