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Poster
On the Estimation Bias in Double Q-Learning
Zhizhou Ren · Guangxiang Zhu · Hao Hu · Beining Han · Jianglun Chen · Chongjie Zhang

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value prediction and improving learning performance. However, as shown by prior work, double Q-learning is not fully unbiased and suffers from underestimation bias. In this paper, we show that such underestimation bias may lead to multiple non-optimal fixed points under an approximate Bellman operator. To address the concerns of converging to non-optimal stationary solutions, we propose a simple but effective approach as a partial fix for the underestimation bias in double Q-learning. This approach leverages an approximate dynamic programming to bound the target value. We extensively evaluate our proposed method in the Atari benchmark tasks and demonstrate its significant improvement over baseline algorithms.

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Author Information

Zhizhou Ren (University of Illinois at Urbana-Champaign)
Guangxiang Zhu (Tsinghua university)
Hao Hu (Tsinghua University, Tsinghua University)
Beining Han (Tsinghua University)
Jianglun Chen (Tsinghua University, Tsinghua University)
Chongjie Zhang (Tsinghua University)

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