Information-Theoretic Equivalence of Entropic Multi-Marginal Optimal Transport: A Theory for Multi-Agent Communication
This work provides a theoretical foundation for multi-agent teaming systems, though it appears incremental as it builds on existing entropic optimal transport theory.
The paper tackles the problem of extending entropic optimal transport to multi-agent communication by proposing an information-theoretic equivalence for multi-marginal optimal transport, generalizing a prior result to prove optimality in multi-agent settings.
In this paper, we propose our information-theoretic equivalence of entropic multi-marginal optimal transport (MOT). This equivalence can be easily reduced to the case of entropic optimal transport (OT). Because OT is widely used to compare differences between knowledge or beliefs, we apply this result to the communication between agents with different beliefs. Our results formally prove the statement that entropic OT is information-theoretically optimal given by Wang et al. [2020] and generalize it to the multi-agent case. We believe that our work can shed light on OT theory in future multi-agent teaming systems.