Researchers formalize learning-to-communicate in multi-agent systems using information structures

Researchers have formalized learning-to-communicate (LTC) in multi-agent systems by bridging deep reinforcement learning and control theory through information structures. The study focuses on quasi-classical (QC) LTCs, demonstrating that non-classical versions are generally intractable. The paper introduces conditions for QC LTCs and develops algorithms with provable complexities for these scenarios. AI

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IMPACT Formalizes learning-to-communicate frameworks, potentially enabling more efficient multi-agent coordination.

RANK_REASON Academic paper on a theoretical aspect of multi-agent communication.

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• arXiv
• Kaiqing Zhang

• paper
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