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A Deep Q-Network Based Approach for Online Bayesian Change Point Detection

Online quickest change-point detection (QCD) plays important role in many applications such as network monitoring, power outage detection, etc. Essentially, the QCD process can be viewed as a partially observable Markov decision process (POMDP). Most of the existing works on QCD assume a priori information about the model. However, in many practical applications, the a priori information of the latent stochastic model is unknown and thus the optimal detection rule is not available. To address this issue, we propose a deep Q-network (DQN) based change-point detection method for the online Bayesian QCD problem when the a priori information is unknown. Numerical results illustrate that the DQN-based method can detect the change point accurately and timely.