DQN Replay Dataset

The DQN Replay Dataset was collected as follows: We first train a DQN agent, on all 60 Atari 2600 games with sticky actions enabled for 200 million frames (standard protocol) and save all of the experience tuples of (observation, action, reward, next observation) (approximately 50 million) encountered during training. We repeat this process five times for each game.

This logged DQN data can be found in the public GCP bucket gs://atari-replay-datasets which can be downloaded using gsutil. To install gsutil, follow the instructions here.

After installing gsutil, run the command to copy the entire dataset:

gsutil -m cp -R gs://atari-replay-datasets/dqn

To run the dataset only for a specific Atari 2600 game (e.g., replace GAME_NAME by Pong to download the logged DQN replay datasets for the game of Pong), run the command:

gsutil -m cp -R gs://atari-replay-datasets/dqn/[GAME_NAME]

This data can be generated by running the online agents using batch_rl/baselines/train.py for 200 million frames (standard protocol). Note that the dataset consists of approximately 50 million experience tuples due to frame skipping (i.e., repeating a selected action for k consecutive frames) of 4. The stickiness parameter is set to 0.25, i.e., there is 25% chance at every time step that the environment will execute the agent’s previous action again, instead of the agent’s new action.


If you find this open source release useful, please reference in your paper:

Agarwal, R., Schuurmans, D. & Norouzi, M.. (2020). An Optimistic Perspective on Offline Reinforcement Learning International Conference on Machine Learning (ICML).

  title={An Optimistic Perspective on Offline Reinforcement Learning},
  author={Agarwal, Rishabh and Schuurmans, Dale and Norouzi, Mohammad},
  journal={International Conference on Machine Learning},


Rishabh Agarwal
Research Associate
Dale Schuurmans
UAlberta, Google
Mohammad Norouzi
Staff Research Scientist

For questions, please contact us at: rishabhagarwal@google.com, mnorouzi@google.com.