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Papers/Rainbow: Combining Improvements in Deep Reinforcement Lear...

Rainbow: Combining Improvements in Deep Reinforcement Learning

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

2017-10-06Reinforcement LearningAtari GamesMontezuma's Revengereinforcement-learning
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Abstract

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.

Results

TaskDatasetMetricValueModel
Atari Gamesatari gameHuman World Record Breakthrough4Rainbow
Atari GamesAtari 2600 Ms. PacmanScore2570.2Rainbow
Atari GamesAtari 2600 Space InvadersScore12629Rainbow
Atari GamesAtari-57Human World Record Breakthrough4Rainbow DQN
Atari GamesAtari 2600 Montezuma's RevengeAverage Return (NoOp)384Rainbow
Video Gamesatari gameHuman World Record Breakthrough4Rainbow
Video GamesAtari 2600 Ms. PacmanScore2570.2Rainbow
Video GamesAtari 2600 Space InvadersScore12629Rainbow
Video GamesAtari-57Human World Record Breakthrough4Rainbow DQN
Video GamesAtari 2600 Montezuma's RevengeAverage Return (NoOp)384Rainbow

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