Learning automata: an introduction
Learning automata: an introduction
Competitive routing in multiuser communication networks
IEEE/ACM Transactions on Networking (TON)
Journal of the ACM (JACM)
How unfair is optimal routing?
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
How much can taxes help selfish routing?
Proceedings of the 4th ACM conference on Electronic commerce
Bounds for the convergence rate of randomized local search in a multiplayer load-balancing game
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Fast convergence of selfish rerouting
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Adaptive routing with stale information
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
Distributed selfish load balancing
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Fast convergence to Wardrop equilibria by adaptive sampling methods
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Linear tolls suffice: new bounds and algorithms for tolls in single source networks
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Recursive Analysis Characterized as a Class of Real Recursive Functions
Fundamenta Informaticae - SPECIAL ISSUE MCU2004
Convergence time to Nash equilibrium in load balancing
ACM Transactions on Algorithms (TALG)
Convergence time to Nash equilibria
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Network games with atomic players
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
Approximating wardrop equilibria with finitely many agents
DISC'07 Proceedings of the 21st international conference on Distributed Computing
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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.