OPERATIONS, TECHNOLOGY, AND INFORMATION MANAGEMENT


Nonstationary Reinforcement Learning: Te Blessing of


(More) Optimism Management Science, 69, 10, October 2023 LINK TO PAPER


RUIHAO ZHU ASSISTANT PROFESSOR


Cornell Peter and Stephanie Nolan School of Hotel Administration


Cornell SC Johnson College of Business Cornell University


Co-authors • Ruihao Zhu


Assistant Professor, Cornell Peter and Stephanie Nolan School of Hotel Administration, Cornell SC Johnson College of Business, Cornell University


• Wang Chi Cheung, National University of Singapore • David Smichi-Levi, Massachusetts Institute of Technology


Summary


Motivated by operations research applications such as inventory control and real-time bidding, Zhu et al. consider undiscounted reinforcement learning in Markov decision processes under model uncertainty and temporal drifts. In this setting, both the latent reward and state transition distributions are allowed to evolve over time, as long as their respective total variations do not exceed certain variation budgets. Tey first develop the sliding window upper confidence bound for reinforcement learning with confidence-widening (SWUCRL2-CW) algorithm and establish its dynamic regret bound when the variation budgets are known. Ten they propose the bandit-over-reinforce- ment learning algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound in a parameter-free manner. Finally, they conduct numerical experiments to show that their proposed algorithms achieve superior empirical performance compared with existing algorithms.


Under nonstationarity, historical data samples may falsely indicate that state transition rarely happens. Tis thus presents a significant challenge when one tries to apply the conventional optimism in the face of uncertainty principle to achieve a low dynamic regret bound. Tey overcome this challenge by pro- posing a novel confidence-widening technique that incorporates additional optimism into their learning algorithms, demonstrating how one can leverage special structures on the state transition distributions to achieve improved dynamic regret bound in time-varying demand environments.


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