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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Barth, D., Bournez, O., Boussaton, O., Cohen, J. (2008). Distributed Learning of Wardrop Equilibria. In: Calude, C.S., Costa, J.F., Freund, R., Oswald, M., Rozenberg, G. (eds) Unconventional Computing. UC 2008. Lecture Notes in Computer Science, vol 5204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85194-3_5
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DOI: https://doi.org/10.1007/978-3-540-85194-3_5
Publisher Name: Springer, Berlin, Heidelberg
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