Game-Theoretic Learning in Distributed Control

Jason R. Marden () and Jeff S. Shamma ()
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Jason R. Marden: University of California, Department of Electrical and Computer Engineering
Jeff S. Shamma: King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Science and Engineering Division (CEMSE)

Chapter 11 in Handbook of Dynamic Game Theory, 2018, pp 511-546 from Springer

Abstract: Abstract In distributed architecture control problems, there is a collection of interconnected decision-making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game-theoretic language, the incentives are defined through utility functions, and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.

Keywords: Learning in games; Evolutionary games; Multiagent systems; Distributed decision systems (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-44374-4_9

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DOI: 10.1007/978-3-319-44374-4_9

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