A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.
Multi-robots gaussian estimation and coverage control: From client–server to peer-to-peer architectures
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A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.