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How to coordinate the communication among intersections effectively in realcomplex traffic scenarios with multi-intersection is challenging. Existingapproaches only enable the communication in a heuristic manner withoutconsidering 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 crucialpredictions of their impact on its neighbors, which improves the communicationefficiency and is universal across existing methods. We also propose a concisenetwork UniLight to make full use of communications enabled by UniComm.Experimental results on real datasets demonstrate that UniComm universallyimproves the performance of existing state-of-the-art methods, and UniLightsignificantly outperforms existing methods on a wide range of trafficsituations.

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