Relational Weight Optimization for Enhancing Team Performance in Multi-Agent Multi-Armed Bandits

TL;DR

This paper proposes a novel approach using FMMC and FDLA algorithms to optimize relational weights in multi-agent multi-armed bandit systems, aiming to accelerate consensus and improve team performance.

Contribution

It introduces a new method for optimizing communication network weights in MAMABs to enhance convergence speed and team efficiency.

Findings

Faster convergence to team consensus in large networks.

Relational weight optimization improves overall team performance.

Communication network structure impacts convergence times.

Abstract

We introduce an approach to improve team performance in a Multi-Agent Multi-Armed Bandit (MAMAB) framework using Fastest Mixing Markov Chain (FMMC) and Fastest Distributed Linear Averaging (FDLA) optimization algorithms. The multi-agent team is represented using a fixed relational network and simulated using the Coop-UCB2 algorithm. The edge weights of the communication network directly impact the time taken to reach distributed consensus. Our goal is to shrink the timescale on which the convergence of the consensus occurs to achieve optimal team performance and maximize reward. Through our experiments, we show that the convergence to team consensus occurs slightly faster in large constrained networks.