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Thompson Sampling has recently been shown to be optimal in the BernoulliMulti-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumesstationary distributions for the rewards. It is often unrealistic to model thereal world as a stationary distribution. In this paper we derive and evaluatealgorithms using Thompson Sampling for a Switching Multi-Armed Bandit Problem.We propose a Thompson Sampling strategy equipped with a Bayesian change pointmechanism to tackle this problem. We develop algorithms for a variety of caseswith constant switching rate: when switching occurs all arms change (GlobalSwitching), switching occurs independently for each arm (Per-Arm Switching),when the switching rate is known and when it must be inferred from data. Thisleads to a family of algorithms we collectively term Change-Point ThompsonSampling (CTS). We show empirical results of the algorithm in 4 artificialenvironments, and 2 derived from real world data; news click-through[Yahoo!,2011] and foreign exchange data[Dukascopy, 2012], comparing them to some otherbandit algorithms. In real world data CTS is the most effective.

Thompson Sampling in Switching Environments with Bayesian Online Change Point Detection