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Distributed Learning of Wardrop Equilibria

Abstract : We consider the problem of learning equilibria in a well known game theoretic traffic model due to Wardrop. We consider a distributed learning algorithm that we prove to converge to equilibria. The proof of convergence is based on a differential equation governing the global macroscopic evolution of the system, inferred from the local microscopic evolutions of agents. We prove that the differential equation converges with the help of Lyapunov techniques.
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Contributor : Johanne Cohen épouse Bournez Connect in order to contact the contributor
Submitted on : Tuesday, July 29, 2008 - 4:17:06 PM
Last modification on : Friday, May 13, 2022 - 10:18:04 PM


  • HAL Id : inria-00308002, version 1



Dominique Barth, Olivier Bournez, Octave Boussaton, Johanne Cohen. Distributed Learning of Wardrop Equilibria. 7th International Conference on Unconventional Computation - UC 2008), Aug 2008, Vienne, Austria. pp.19--32. ⟨inria-00308002⟩



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