Search Machine Learning Repository: @inproceedings{icml2014c2_krichene14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {On the convergence of no-regret learning in selfish routing},
    Url = {http://jmlr.org/proceedings/papers/v32/krichene14.pdf},
    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.},
    Author = {Walid Krichene and Benjamin Drigh├Ęs and Alexandre Bayen},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {163-171}
   }