Distributed Regret Matching Algorithm for Dynamic Congestion Games with Information Provision

The advances in adaptive learning dynamics to pure Nash equilibria in game theory provide promising results for the modeling of selfish agents with limited information in congestion games. In this study, a distributed game-theoretical learning algorithm with real-time information provision for dynamic congestion games is proposed. The learning algorithm is based on the regret matching process by considering a user's previously realised payoffs and real-time information. The numerical studies show that the proposed algorithm can converge to a non-cooperative Nash equilibrium in both static and dynamic congestion networks. Moreover, the proposed algorithm leads to a plausible real-time route choice modeling framework based on a user's perception being updated by incorporating the user's past experience, real-time information and behaviour inertia.