Model-Free Nonstationary Reinforcement Learning: Near-Optimal Regret and Applications in Multiagent Reinforcement Learning and Inventory Control

We consider model-free reinforcement learning (RL) in nonstationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We propose Restarted Q-Learning with Upper Confidence Bounds (RestartQ-UCB), the first model-free algorithm for nonstationary RL, and show that it outperforms existing solutions in terms of dynamic regret. Specifically, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret bound of [Formula: see text], where S and A are the numbers of states and actions, respectively, [Formula: see text] is the variation budget, H is the number of time steps per episode, and T is the total number of time steps. We further present a parameter-free algorithm named Double-Restart Q-UCB that does not require prior knowledge of the variation budget. We show that our algorithms are nearly optimal by establishing an information-theoretical lower bound of [Formula: see text], the first lower bound in nonstationary RL. Numerical experiments validate the advantages of RestartQ-UCB in terms of both cumulative rewards and computational efficiency. We demonstrate the power of our results in examples of multiagent RL and inventory control across related products. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: The research of D. Simchi-Levi and R. Zhu was supported by the MIT Data Science Laboratory. The research of W. Mao, K. Zhang, and T. Başar was supported in part by the U.S. Army Research Laboratory (ARL) Cooperative Agreement W911NF-17-2-0196, in part by the Office of Naval Research (ONR) [MURI Grant N00014-16-1-2710], and in part by the Air Force Office of Scientific Research (AFOSR) [Grant FA9550-19-1-0353]. K. Zhang also acknowledges support from U.S. Army Research Laboratory (ARL) [Grant W911NF-24-1-0085]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02533 .

• 저널Management Science · 71(2) · 1564–1580
• 토픽Advanced Bandit Algorithms Research · Management Science and Operations Research
• DOI10.1287/mnsc.2022.02533
• 저자Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi‐Levi, Tamer Başar