Journal
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 7, 2020
Volume 7, Issue -, Pages 279-301Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev-statistics-031219-041220
Keywords
reinforcement learning; dynamic treatment regimes; model-free; causal inference; policy search
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Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we (a) review the history of Q-learning in computer science and statistics, (b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and (c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.
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