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

Monish Reddy Kotturu, Saniya Vahedian Movahed, Paul Robinette, Kshitij Jerath, Amanda Redlich +1 more
10/30/2024
cs.MA

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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.

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Cite this paper

@article{kotturu2024relational,
  title  = {Relational Weight Optimization for Enhancing Team Performance in Multi-Agent Multi-Armed Bandits},
  author = {Monish Reddy Kotturu and Saniya Vahedian Movahed and Paul Robinette and Kshitij Jerath and Amanda Redlich and Reza Azadeh},
  year   = {2024},
  eprint = {2410.23379},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.23379v1}
}

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