Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication
Multi-agent coordination under partial observability requires agents to share complementary private information. While recent methods optimize messages for intermediate objectives (e.g., reconstruction accuracy or mutual information), rather than decision quality, we introduce \textbf{SeqComm-DFL}, unifying the sequential communication with decision-focused learning for task performance. Our approach features \emph{value-aware message generation with sequential Stackelberg conditioning}: messages maximize receiver decision quality and are generated in priority order, with agents conditioning on their predecessors. The \emph{guidance potential} determined by their prosocial ordering. We ex...
| Authors | Benjamin Amoh, Geoffrey Parker, Wesley Marrero |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 12 May 2026 |
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