Name
Abstract | ||
|---|---|---|
We study the repeated, non-atomic routing game, in which selfish players make a sequence of routing decisions. We consider a model in which players use regret-minimizing algorithms as the learning mechanism, and study the resulting dynamics. We are concerned in particular with the convergence to the set of Nash equilibria of the routing game. No-regret learning algorithms are known to guarantee convergence of a subsequence of population strategies. We are concerned with convergence of the actual sequence. We show that convergence holds for a large class of online learning algorithms, inspired from the continuous-time replicator dynamics. In particular, the discounted Hedge algorithm is proved to belong to this class, which guarantees its convergence. |
Year | Venue | Field |
|---|---|---|
2014 | ICML | Convergence (routing),Online learning,Population,Mathematical optimization,Regret,Computer science,Replicator equation,Artificial intelligence,Subsequence,Nash equilibrium,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Walid Krichene | 1 | 108 | 14.02 |
| Benjamin Drighès | 2 | 10 | 0.98 |
| Alexandre M. Bayen | 3 | 1250 | 137.72 |