Posterior-Driven Actor-Critic Framework for Active Hypothesis Testing
Fields, Greg; Javidi, Tara
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https://hdl.handle.net/2142/130328
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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