AN ACTOR-CRITIC REINFORCEMENT LEARNING APPROACH TO MINIMUM AGE OF INFORMATION SCHEDULING IN ENERGY HARVESTING NETWORKS

被引：16

作者：

Leng, Shiyang ^{[1
]}

Yener, Aylin ^{[2
]}

机构：

[1] Penn State Univ, Elect Engn Dept, University Pk, PA 16802 USA

[2] Ohio State Univ, Elect & Comp Engn Dept, Columbus, OH 43210 USA

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) | 2021年

关键词：

Age of information; energy harvesting; user scheduling; actor-critic deep reinforcement learning;

D O I：

10.1109/ICASSP39728.2021.9415110

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We study age of information (AoI) minimization in a network consisting of energy harvesting transmitters that are scheduled to send status updates to their intended receivers. We consider the user scheduling problem over a communication session. To solve online user scheduling with causal knowledge of the system state, we formulate an infinite-state Markov decision problem and adopt model-free on-policy deep reinforcement learning (DRL), where the actor-critic algorithm with deep neural network function approximation is implemented. Comparable AoI to the offline optimal is demonstrated, verifying the efficacy of learning for AoI-focused scheduling and resource allocation problems in wireless networks.

引用

页码：8128 / 8132

页数：5

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