Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming. (arXiv:2201.08484v1 [cs.MA])

Information sharing is key in building team cognition and enables
coordination and cooperation. High-performing human teams also benefit from
acting strategically with hierarchical levels of iterated communication and
rationalizability, meaning a human agent can reason about the actions of their
teammates in their decision-making. Yet, the majority of prior work in
Multi-Agent Reinforcement Learning (MARL) does not support iterated
rationalizability and only encourage inter-agent communication, resulting in a
suboptimal equilibrium cooperation strategy. In this work, we show that
reformulating an …

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