ROMA-iQSS: An Objective Alignment Approach via State-Based Value Learning and ROund-Robin Multi-Agent Scheduling
CoRR(2024)
摘要
Effective multi-agent collaboration is imperative for solving complex,
distributed problems. In this context, two key challenges must be addressed:
first, autonomously identifying optimal objectives for collective outcomes;
second, aligning these objectives among agents. Traditional frameworks, often
reliant on centralized learning, struggle with scalability and efficiency in
large multi-agent systems. To overcome these issues, we introduce a
decentralized state-based value learning algorithm that enables agents to
independently discover optimal states. Furthermore, we introduce a novel
mechanism for multi-agent interaction, wherein less proficient agents follow
and adopt policies from more experienced ones, thereby indirectly guiding their
learning process. Our theoretical analysis shows that our approach leads
decentralized agents to an optimal collective policy. Empirical experiments
further demonstrate that our method outperforms existing decentralized
state-based and action-based value learning strategies by effectively
identifying and aligning optimal objectives.
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