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arXiv · 2306.10158

Learning-Augmented Decentralized Online Convex Optimization in Networks

Pengfei Li·Jianyi Yang·Adam Wierman·Shaolei Ren
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Abstract

This paper studies decentralized online convex optimization in a networked multi-agent system and proposes a novel algorithm, Learning-Augmented Decentralized Online optimization (LADO), for individual agents to select actions only based on local online information. LADO leverages a baseline policy to safeguard online actions for worst-case robustness guarantees, while staying close to the machine learning (ML) policy for average performance improvement. In stark contrast with the existing learning-augmented online algorithms that focus on centralized settings, LADO achieves strong robustness guarantees in a decentralized setting. We also prove the average cost bound for LADO, revealing the tradeoff between average performance and worst-case robustness and demonstrating the advantage of training the ML policy by explicitly considering the robustness requirement.

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Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren. 2023-06-16. Learning-Augmented Decentralized Online Convex Optimization in Networks. https://arxiv.org/abs/2306.10158

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