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cleanrl package with the following command:pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id QbertNoFrameskip-v41curl -OL https://huggingface.co/cleanrl/QbertNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn_atari.py
2curl -OL https://huggingface.co/cleanrl/QbertNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
3curl -OL https://huggingface.co/cleanrl/QbertNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
4poetry install --all-extras
5python dqn_atari.py --capture-video --env-id QbertNoFrameskip-v4 --seed 1 --save-model --upload-model --hf-entity cleanrl --track1{'batch_size': 32,
2 'buffer_size': 1000000,
3 'capture_video': True,
4 'cuda': True,
5 'end_e': 0.01,
6 'env_id': 'QbertNoFrameskip-v4',
7 'exp_name': 'dqn_atari',
8 'exploration_fraction': 0.1,
9 'gamma': 0.99,
10 'hf_entity': 'cleanrl',
11 'learning_rate': 0.0001,
12 'learning_starts': 80000,
13 'num_envs': 1,
14 'save_model': True,
15 'seed': 1,
16 'start_e': 1,
17 'target_network_frequency': 1000,
18 'tau': 1.0,
19 'torch_deterministic': True,
20 'total_timesteps': 10000000,
21 'track': True,
22 'train_frequency': 4,
23 'upload_model': True,
24 'wandb_entity': None,
25 'wandb_project_name': 'cleanRL'}