Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

Understand

How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging.

• Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared.
• In this paper, we propose a universal communication form UniComm between intersections.
• UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods.