Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach

The problem of single-agent Evasive AHT (EAHT), Active Hypothesis Testing (AHT) refers to the family of where a passive Eavesdropper (Eve) collects noisy estimates problems where one legitimate agent or decision maker, or a of the legit observations and tries to infer the underlying group of collaborating agents or decision makers, adaptively hypothesis, was studied in [24], focusing however explicitly select(s) sensing actions and collect(s) observations in order on the asymptotical case. In that work, the authors formulated to infer the underlying true hypothesis in a fast and reliable single-agent EAHT as a constrained optimization problem manner [1], [2]. AHT and related problems, such as active including the legitimate agent's and the Eavesdropper's (Eve) parameter estimation [3] and active change point detection [4], error exponent. However, near-optimal or optimal action selection [5], find numerous applications in wireless communications, policies were not presented. In this paper, motivated including anomaly detection over sensor networks [6], strong by the lack of explicit policies for EAHT, we present novel or weak radar models for target detection [7], cyber-intrusion single-and multi-agent EAHT approaches for wireless sensor detection, target search, and adaptive beamforming [8], as well networks that are based on a deep NeuroEvolution (NE) as, very recently, RIS-enabled localization [9] and channel framework. Our contributions are summarized as follows: estimation [10]. In addition, AHT is closely related to the 1) We formulate the single-agent EAHT problem studied feedback channel coding problem [11].