NDLI: Perturbed learning automata in potential games

Content Provider	IEEE Xplore Digital Library
Author	Chasparis, G.C. Shamma, J.S. Rantzer, A.
Copyright Year	2011
Description	Author affiliation: Department of Automatic Control, Lund University, 221 00-SE, Sweden (Chasparis, G.C.; Rantzer, A.) \|\| School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA (Shamma, J.S.)
Abstract	This paper presents a reinforcement learning algorithm and provides conditions for global convergence to Nash equilibria. For several reinforcement learning schemes, including the ones proposed here, excluding convergence to action profiles which are not Nash equilibria may not be trivial, unless the step-size sequence is appropriately tailored to the specifics of the game. In this paper, we sidestep these issues by introducing a new class of reinforcement learning schemes where the strategy of each agent is perturbed by a state-dependent perturbation function. Contrary to prior work on equilibrium selection in games, where perturbation functions are globally state dependent, the perturbation function here is assumed to be local, i.e., it only depends on the strategy of each agent. We provide conditions under which the strategies of the agents will converge to an arbitrarily small neighborhood of the set of Nash equilibria almost surely. We further specialize the results to a class of potential games.
Starting Page	2453
Ending Page	2458
File Size	217882
Page Count	6
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6161294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Games Convergence Nash equilibrium Vectors Learning Learning systems Sensitivity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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