Papers › Rainbow: Combining Improvements in Deep Reinforcement Learning

Rainbow: Combining Improvements in Deep Reinforcement Learning

6 Oct 2017arXiv:1710.02298archive 2025-07-28

Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver

The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.

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BY571/DQN-Atari-Agents mentioned on GitHubpytorch report
Curt-Park/rainbow-is-all-you-need mentioned on GitHubMIT report
Kaixhin/Rainbow mentioned on GitHubpytorchMIT report
amzoyang/CS-221-Final-Project mentioned on GitHubpytorch report
atavakol/action-hypergraph-networks mentioned on GitHubtfMIT report
chainer/chainerrl mentioned on GitHubpytorch report
coreylowman/rl_simply mentioned on GitHubpytorch report
daviddcho/supermario mentioned on GitHubpytorch report
deconlabs/Binanace-trading-simulation mentioned on GitHubpytorchMIT report
deconlabs/Binanace_trading_simulation mentioned on GitHubpytorchMIT report
dsapandora/s_cera mentioned on GitHubtfGPL-3.0 report
eddynelson/dqn mentioned on GitHubtf report
efg59/Rainbow mentioned on GitHub report
facebookresearch/Horizon mentioned on GitHubpytorch report
facebookresearch/ReAgent mentioned on GitHubpytorch report
floringogianu/atari-agents mentioned on GitHubpytorch report
htdt/rainbow mentioned on GitHubpytorch report
jacobkooi/hadamax mentioned on GitHubjax report
lireer/ricochet-robot-solver mentioned on GitHub report
liuyuezhang/pyrl mentioned on GitHubpytorch report
michaelnny/deep_rl_zoo mentioned on GitHubpytorch report
mindspore-courses/Rainbow-MindSpore mentioned on GitHubmindspore report
mohith-sakthivel/rainbow_dqn mentioned on GitHubpytorchGPL-3.0 report
robintyh1/icml2021-pengqlambda mentioned on GitHubtfMIT report
roboticist-by-day/Rainbow_DQN mentioned on GitHubpytorchGPL-3.0 report
thu-ml/tianshou mentioned on GitHubpytorchMIT report
xusophia/DataSciFinalProj mentioned on GitHubpytorch report

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straight_through_round jacobkooi/hadamax/purejaxql/pqn_atari_hadamax.py community (archive-listed) ran · violated contract fingerprinted Apache-2.0 (permissive) · 3f5569362eb33902 · report
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linear_schedule identical code first harvested elsewhere ran · honoured contract fingerprinted licence of this copy not recorded · 4c60d8998b722013 · report

Tasks

Atari GamesDeep Reinforcement LearningMontezuma's RevengeReinforcement LearningReinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Atari Games Atari 2600 Ms. Pacman Rainbow Score 2570.2 #31 of 47 Archive leaderboard report
Atari Games Atari 2600 Space Invaders Rainbow Score 12629.0 #17 of 55 Archive leaderboard report
Atari Games Atari games Rainbow DQN Mean Human Normalized Score 873.97% #10 of 12 Archive leaderboard report
Atari Games Atari-57 Rainbow DQN Human World Record Breakthrough 4 #9 of 11 Archive leaderboard report
Atari Games Atari-57 Rainbow DQN Mean Human Normalized Score 873.97% #9 of 11 Archive leaderboard report
Atari Games atari game Rainbow Human World Record Breakthrough 4 #9 of 9 Archive leaderboard report
Montezuma's Revenge Atari 2600 Montezuma's Revenge Rainbow Average Return (NoOp) 384 #3 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: Rainbow DQN

AdamConvolutionDQNDense ConnectionsDouble Q-learningDueling NetworkN-step ReturnsNoisy Linear LayerPrioritized Experience ReplayQ-LearningRainbow DQN

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