Testing Stationarity and Change Point Detection in Reinforcement Learning
This work addresses the restrictive stationarity assumption in RL for applications like traffic control and mobile health, offering an incremental improvement by integrating detection with existing methods.
The paper tackles the problem of nonstationary environments in offline reinforcement learning by developing a consistent test for nonstationarity of the optimal Q-function and a sequential change point detection method, which is validated through theoretical results, simulations, and a real-world dataset from the 2018 Intern Health Study.
We consider offline reinforcement learning (RL) methods in possibly 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. In this paper, we develop a consistent procedure to test the nonstationarity of the optimal Q-function 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 optimization 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 https://github.com/limengbinggz/CUSUM-RL.