Title
Learning Nash Equilibria in Congestion Games.
Abstract
We study the repeated congestion game, in which multiple populations of players share resources, and make, at each iteration, a decentralized decision on which resources to utilize. We investigate the following question: given a model of how individual players update their strategies, does the resulting dynamics of strategy profiles converge to the set of Nash equilibria of the one-shot game? We consider in particular a model in which players update their strategies using algorithms with sublinear discounted regret. We show that the resulting sequence of strategy profiles converges to the set of Nash equilibria in the sense of Ces\`aro means. However, strong convergence is not guaranteed in general. We show that strong convergence can be guaranteed for a class of algorithms with a vanishing upper bound on discounted regret, and which satisfy an additional condition. We call such algorithms AREP algorithms, for Approximate REPlicator, as they can be interpreted as a discrete-time approximation of the replicator equation, which models the continuous-time evolution of population strategies, and which is known to converge for the class of congestion games. In particular, we show that the discounted Hedge algorithm belongs to the AREP class, which guarantees its strong convergence.
Year
Venue
Field
2014
arXiv: Learning
Sublinear function,Population,Mathematical optimization,Congestion game,Mathematical economics,Regret,Upper and lower bounds,Best response,Replicator equation,Nash equilibrium,Mathematics
DocType
Volume
Citations 
Journal
abs/1408.0017
2
PageRank 
References 
Authors
0.42
5
3
Name
Order
Citations
PageRank
Walid Krichene110814.02
Benjamin Drighès220.42
Alexandre M. Bayen31250137.72