Distributed Learning for Dynamic Congestion Games: Balancing Exploration and Exploitation to Minimize Long-Term Social Cost

The core message of this article is to study how users can efficiently learn and share traffic information in a distributed manner to minimize the long-term social cost in dynamic congestion games, where users' routing decisions not only affect their own travel costs but also dynamically alter the traffic conditions for future users.