Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach
arxiv(2024)
摘要
In this paper, we focus on one centralized and one decentralized problem of
active hypothesis testing in the presence of an eavesdropper. For the
centralized problem including a single legitimate agent, we present a new
framework based on NeuroEvolution (NE), whereas, for the decentralized problem,
we develop a novel NE-based method for solving collaborative multi-agent tasks,
which interestingly maintains all computational benefits of single-agent NE.
The superiority of the proposed EAHT approaches over conventional active
hypothesis testing policies, as well as learning-based methods, is validated
through numerical investigations in an example use case of anomaly detection
over wireless sensor networks.
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