2023/06/19 by Jing Dong, Jingyu Wu, Dong, Jing +7 · 1 citation
Decision Sciences · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Game Theory and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2306.13673
openalex publication_date 2023/06/19 · openalex created_date 2023/06/29 · openalex updated_date 2026/07/28
The congestion game is a powerful model that encompasses a range of engineering systems such as traffic networks and resource allocation. It describes the behavior of a group of agents who share a common set of F facilities and take actions as subsets with k facilities. In this work, we study the online formulation of congestion games, where agents participate in the game repeatedly and observe feedback with randomness. We propose CongestEXP, a decentralized algorithm that applies the classic exponential weights method. By maintaining weights on the facility level, the regret bound of CongestEXP avoids the exponential dependence on the size of possible facility sets, i.e., \binomFk ≈ Fk, and scales only linearly with F. Specifically, we show that CongestEXP attains a regret upper bound of O(kF√(T)) for every individual player, where T is the time horizon. On the other hand, exploiting the exponential growth of weights enables CongestEXP to achieve a fast convergence rate. If a strict Nash equilibrium exists, we show that CongestEXP can converge to the strict Nash policy almost exponentially fast in O(Fexp(-t1-α)), where t is the number of iterations and α∈ (1/2, 1).