Title: Reinforcement learning in possibly nonstationary environments
Authors: Zhenke Wu - University of Michigan at Ann Arbor (United States) [presenting]
Mengbing Li - University of Michigan (United States)
Chengchun Shi - LSE (United Kingdom)
Piotr Fryzlewicz - London School of Economics (United Kingdom)
Abstract: Reinforcement learning (RL) methods are considered in offline nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption that requires the system transition and the reward function to be constant over time. However, the stationarity assumption is restrictive in practice and is likely to be violated in a number of applications, including traffic signal control, robotics and mobile health. We introduce a consistent procedure to test the nonstationarity of the optimal policy based on pre-collected historical data, without additional online data collection. Based on the proposed test, we further develop a sequential change point detection method that can be naturally coupled with existing state-of-the-art RL methods for policy optimisation in nonstationary environments. The usefulness of our method is illustrated by theoretical results, simulation studies, and a real data example from the 2018 Intern Health Study. A Python implementation of the proposed procedure is available at GitHub.