How can agents collaborate to optimize rewards while staying within a cost budget?
Cooperative Multi-Agent Constrained Stochastic Linear Bandits
October 24, 2024
https://arxiv.org/pdf/2410.17382This paper studies how a network of AI agents can collaborate to learn the best actions to take in a system, where each agent only sees a small part of the system and can only communicate with its neighbors. The goal is to maximize rewards while staying within a certain cost limit.
- Relevance to LLM-based multi-agent systems: The proposed algorithm, MA-OPLB, offers a framework for decentralized learning and decision-making, where agents (potentially LLMs) with limited communication can collaborate to solve a complex problem.
- The paper's focus on constrained optimization, specifically staying within a cost budget, is highly relevant for resource-intensive LLM applications.
- The analysis of regret bounds provides insights into the efficiency of such systems and their scalability as the network size grows.