Multiagent Learning Paradigms

Abstract “Perhaps a thing is simple if you can describe it fully in several different ways, without immediately knowing that you are describing the same thing” – Richard Feynman This articles examines multiagent learning from several paradigmatic perspectives, aiming to bring them together within one framework. We aim to provide a general definition of multiagent learning and lay out the essential characteristics of the various paradigms in a systematic manner by dissecting multiagent learning into its main components. We show how these various paradigms are related and describe similar learning processes but from varying perspectives, e. g. an individual (cognitive) learner vs. a population of (simple) learning agents.

Authors

• Karl Tuyls
• Peter Stone 0001

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