(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.
Command to reproduce the training
curl -OL https://huggingface.co/cleanrl/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn.py
curl -OL https://huggingface.co/cleanrl/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --track --capture-video --save-model --upload-model --hf-entity cleanrl --env-id PongNoFrameskip-v4 --seed 1
Hyperparameters
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0001,
'learning_starts': 80000,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
Evaluation results
- mean_reward on PongNoFrameskip-v4self-reported17.80 +/- 1.83