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- W4295800956 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." @default.
- W4295800956 created "2022-09-15" @default.
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- W4295800956 date "2017-10-06" @default.
- W4295800956 modified "2023-10-17" @default.
- W4295800956 title "Rainbow: Combining Improvements in Deep Reinforcement Learning" @default.
- W4295800956 doi "https://doi.org/10.48550/arxiv.1710.02298" @default.
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