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Traffic signal control is an emerging application scenario for reinforcementlearning. Besides being as an important problem that affects people's dailylife in commuting, traffic signal control poses its unique challenges forreinforcement learning in terms of adapting to dynamic traffic environment andcoordinating thousands of agents including vehicles and pedestrians. A keyfactor in the success of modern reinforcement learning relies on a goodsimulator to generate a large number of data samples for learning. The mostcommonly used open-source traffic simulator SUMO is, however, not scalable tolarge road network and large traffic flow, which hinders the study ofreinforcement learning on traffic scenarios. This motivates us to create a newtraffic simulator CityFlow with fundamentally optimized data structures andefficient algorithms. CityFlow can support flexible definitions for roadnetwork and traffic flow based on synthetic and real-world data. It alsoprovides user-friendly interface for reinforcement learning. Most importantly,CityFlow is more than twenty times faster than SUMO and is capable ofsupporting city-wide traffic simulation with an interactive render formonitoring. Besides traffic signal control, CityFlow could serve as the basefor other transportation studies and can create new possibilities to testmachine learning methods in the intelligent transportation domain.

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario