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arXiv · 2002.12399

ConQUR: Mitigating Delusional Bias in Deep Q-learning

Andy Su·Jayden Ooi·Tyler Lu·Dale Schuurmans·Craig Boutilier
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Abstract

Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "consistent" with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.

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Andy Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier. 2020-02-27. ConQUR: Mitigating Delusional Bias in Deep Q-learning. https://arxiv.org/abs/2002.12399

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