Datasets › DQN Replay Dataset
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.
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.
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 31 papers for it but never published that list.
Dataset loaders archive 2025-07-28
3 loaders as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- Atari-57
- DQN Replay Dataset
2 variant names, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections