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.

本文针对中央化和去中心化的积极假设测试问题，提出了基于神经进化的新框架和解决协作多智能体任务的新方法。在无线传感器网络异常检测示例应用中，通过数值实验验证了所提出的 EAHT 方法相对于传统积极假设测试策略和基于学习的方法的优越性。

单智能体和多智能体私人主动感知：一种深度神经进化方法

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

We consider a decentralized formulation of the active hypothesis testing
(AHT) problem, where multiple agents gather noisy observations from the
environment with the purpose of identifying the correct hypothesis. At each
time step, agents have the option to select a sampling action. These different
actions result in observations drawn from various distributions, each
associated with a specific hypothesis. The agents collaborate to accomplish the
task, where message exchanges between agents are allowed over a rate-limited
communications channel. The objective is to devise a multi-agent policy that
minimizes the Bayes risk. This risk comprises both the cost of sampling and the
joint terminal cost incurred by the agents upon making a hypothesis
declaration. Deriving optimal structured policies for AHT problems is generally
mathematically intractable, even in the context of a single agent. As a result,
recent efforts have turned to deep learning methodologies to address these
problems, which have exhibited significant success in single-agent learning
scenarios. In this paper, we tackle the multi-agent AHT formulation by
introducing a novel algorithm rooted in the framework of deep multi-agent
reinforcement learning. This algorithm, named Multi-Agent Reinforcement
Learning for AHT (MARLA), operates at each time step by having each agent map
its state to an action (sampling rule or stopping rule) using a trained deep
neural network with the goal of minimizing the Bayes risk. We present a
comprehensive set of experimental results that effectively showcase the agents'
ability to learn collaborative strategies and enhance performance using MARLA.
Furthermore, we demonstrate the superiority of MARLA over single-agent learning
approaches. Finally, we provide an open-source implementation of the MARLA
framework, for the benefit of researchers and developers in related domains.

我们提出了一种基于深度多智能体强化学习框架的算法，名为 MARLA，用于多智能体主动假设检验问题，通过将每个智能体的状态映射到行动，从而最小化贝叶斯风险。我们通过实验结果有效展示了智能体学习协作策略和通过 MARLA 提升性能的能力，并展示了 MARLA 在单个智能体学习方法上的优越性。最后，我们提供了 MARLA 框架的开源实现，以方便相关领域的研究人员和开发者。

深度多智能体强化学习在去中心化主动假设测试中的应用

Deep Multi-Agent Reinforcement Learning for Decentralized Active  Hypothesis Testing

We consider the problem of noisy Bayesian active learning, where we are given
a finite set of functions $\mathcal{H}$, a sample space $\mathcal{X}$, and a
label set $\mathcal{L}$. One of the functions in $\mathcal{H}$ assigns labels
to samples in $\mathcal{X}$. The goal is to identify the function that
generates the labels even though the result of a label query on a sample is
corrupted by independent noise. More precisely, the objective is to declare one
of the functions in $\mathcal{H}$ as the true label generating function with
high confidence using as few label queries as possible, by selecting the
queries adaptively and in a strategic manner.
Previous work in Bayesian active learning considers Generalized Binary
Search, and its variants for the noisy case, and analyzes the number of queries
required by these sampling strategies. In this paper, we show that these
schemes are, in general, suboptimal. Instead we propose and analyze an
alternative strategy for sample collection. Our sampling strategy is motivated
by a connection between Bayesian active learning and active hypothesis testing,
and is based on querying the label of a sample which maximizes the Extrinsic
Jensen-Shannon divergence at each step. We provide upper and lower bounds on
the performance of this sampling strategy, and show that these bounds are
better than previous bounds.

本文提出了一种基于最大化外部 Jensen-Shannon 分歧的标签查询采样策略，用于噪声贝叶斯主动学习中的真实标签生成函数的识别，并提供了上下界，表现优于以往的方法。