# Learning-Augmented Decentralized Online Convex Optimization in Networks

arXiv: https://arxiv.org/abs/2306.10158 · HTML: https://mlsanity.com/paper/2306.10158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 76 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 30 of 38)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Exploratory Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1909.04063/) (2019)
- [The Primal-Dual method for Learning Augmented Algorithms](https://mlsanity.com/paper/2010.11632/) (2020)
- [Online metric algorithms with untrusted predictions](https://mlsanity.com/paper/2003.02144/) (2020)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms](https://mlsanity.com/paper/2010.11443/) (2020)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Conservative Bandits](https://mlsanity.com/paper/1602.04282/) (2016)
- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [Control Regularization for Reduced Variance Reinforcement Learning](https://mlsanity.com/paper/1905.05380/) (2019)
- [Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent](https://mlsanity.com/paper/1803.10366/) (2018)
- [Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization](https://mlsanity.com/paper/1905.12776/) (2019)
- [Revisiting Smoothed Online Learning](https://mlsanity.com/paper/2102.06933/) (2021)
- [Conservative Exploration in Reinforcement Learning](https://mlsanity.com/paper/2002.03218/) (2020)
- [Chasing Convex Bodies and Functions with Black-Box Advice](https://mlsanity.com/paper/2206.11780/) (2022)
- [Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity](https://mlsanity.com/paper/2210.12312/) (2022)
- [Online Distributed Optimization on Dynamic Networks](https://mlsanity.com/paper/1412.7215/) (2014)
- [Stability Constrained Reinforcement Learning for Real-Time Voltage Control](https://mlsanity.com/paper/2109.14854/) (2021)
- [Robust Learning-Augmented Caching: An Experimental Study](https://mlsanity.com/paper/2106.14693/) (2021)
- [A Regression Approach to Learning-Augmented Online Algorithms](https://mlsanity.com/paper/2205.08717/) (2022)
- [A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networks](https://mlsanity.com/paper/1809.04374/) (2018)
- [Smoothed Online Optimization with Unreliable Predictions](https://mlsanity.com/paper/2202.03519/) (2022)
- [Smooth Imitation Learning for Online Sequence Prediction](https://mlsanity.com/paper/1606.00968/) (2016)
- [A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning](https://mlsanity.com/paper/2106.11692/) (2021)
- [Distributed Online Convex Optimization with an Aggregative Variable](https://mlsanity.com/paper/2007.06844/) (2020)

## Built on by (most cited first, 1 of 1)

- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)

## Abstract (arXiv)

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.