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Learning Automata in Games with Memory with Application to Circuit-Switched Routing
 

Summary: Learning Automata in Games with Memory with Application to
Circuit-Switched Routing
Murat Alanyali
Abstract-- A general setting is considered in which au-
tonomous users interact by means of a finite-state controlled
Markov process. This process is driven by the collective actions
of all users, and individual users receive separate rewards
according to its state. It is assumed that each user chooses its
actions via a reinforcement learning algorithm based on its
local information. The dynamic behavior of user strategies
is characterized for small values of a step-size parameter
adopted in learning. The general form of equilibria is obtained
and is shown to be analogous to Wardrop equilibria if users
update their strategies on a faster time-scale compared to the
underlying process. The results are illustrated in the context
of routing in circuit-switched communication networks.
I. INTRODUCTION
We consider a dynamic setting in which a collection of
users interact by means of an underlying Markov process
that is controlled by the collective actions of all users. The

  

Source: Alanyali, Murat - Department of Electrical and Computer Engineering, Boston University

 

Collections: Engineering