Posterior-Driven Actor-Critic Framework for Active Hypothesis Testing

Fields, Greg; Javidi, Tara

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https://hdl.handle.net/2142/130328 Copy

Description

Title

Posterior-Driven Actor-Critic Framework for Active Hypothesis Testing

Author(s)

• Fields, Greg
• Javidi, Tara

Issue Date

2025-09-17

Keyword(s)

• Active hypothesis testing
• Deep learning
• Quantum state discrimination

Abstract

We propose a readily adaptable, general purpose framework for learning active hypothesis testing policies across a wide variety of problem settings. Our PostAC framework trains a sequential, adaptive policy which tracks a Bayesian posterior over hypotheses and, at each time-step, uses a deep neural network to calculate the policy’s actions as a function of this posterior. We first lay out an actor-critic algorithm that efficiently trains these DNN policies and then apply our algorithm to two problems: channel coding with feedback, where we match the performance of an analytically derived state-of-the-art coding scheme, and the problem of coherent state discrimination for optical communication, where we show state-of-the-art performance in the low power regime.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130328&&

Copyright and License Information

Copyright 2025 is held by Greg Fields and Tara Javidi.

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