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Mixed incentives among a population with multiagent teams has been shown tohave advantages over a fully cooperative system; however, discovering the bestmixture of incentives or team structure is a difficult and dynamic problem. Wepropose a framework where individual learning agents self-regulate theirconfiguration of incentives through various parts of their reward function.This work extends previous work by giving agents the ability to dynamicallyupdate their group alignment during learning and by allowing teammates to havedifferent group alignment. Our model builds on ideas from hierarchicalreinforcement learning and meta-learning to learn the configuration of a rewardfunction that supports the development of a behavioral policy. We providepreliminary results in a commonly studied multiagent environment and find thatagents can achieve better global outcomes by self-tuning their respective groupalignment parameters.

Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams