How to optimize communication for faster team consensus in multi-agent bandits?
Relational Weight Optimization for Enhancing Team Performance in Multi-Agent Multi-Armed Bandits
November 1, 2024
https://arxiv.org/pdf/2410.23379This paper explores optimizing communication in multi-agent reinforcement learning (specifically Multi-Agent Multi-Armed Bandits or MAMABs) by adjusting the weights of connections in the agent network. It aims to speed up consensus among agents about the best action to take in an uncertain environment, improving overall team performance.
For LLM-based multi-agent systems, this research highlights the importance of efficient communication network topologies for achieving faster convergence on optimal solutions. Optimizing the flow of information (analogous to edge weights) between LLMs acting as agents could significantly impact how quickly they reach agreement and effectively collaborate.