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Multiagent Learning Paradigms

  • K. Tuyls
  • P. Stone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10767)

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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.DeepMindParisFrance
  2. 2.University of LiverpoolLiverpoolUK
  3. 3.University of TexasAustinUSA

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