Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing

TL;DR

This paper introduces a novel actor-critic reinforcement learning approach for real-time anomaly detection in stochastic processes, optimizing the decision-making process for probing and alerting.

Contribution

It develops a Bayesian Markov decision process framework combined with deep actor-critic RL for efficient anomaly detection, outperforming traditional methods.

Findings

Superior detection performance demonstrated in numerical experiments

Effective sequential decision-making for anomaly threshold crossing

Enhanced adaptability over model-based algorithms

Abstract

We address the problem of monitoring a set of binary stochastic processes and generating an alert when the number of anomalies among them exceeds a threshold. For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states (normal or anomalous). Based on the received observations, the decisionmaker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations. When the decision is to continue, it then decides whether to collect observations at the next time instant or defer it to a later time. If it chooses to collect observations, it further determines the subset of processes to be probed. To devise this three-step sequential decision-making process, we use a Bayesian formulation wherein we learn the posterior probability on the states of the processes. Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning. Via numerical experiments, we demonstrate the superior performance of our algorithm compared to the traditional model-based algorithms.