On the Estimation Bias in Double Q-Learning
This addresses a specific issue in reinforcement learning for improving algorithm stability and performance, but it is incremental as it builds on existing double Q-learning methods.
The paper tackles the underestimation bias in double Q-learning, which can lead to non-optimal fixed points, and proposes a method using approximate dynamic programming to bound target values, showing significant improvement in Atari benchmark tasks.
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.