Introduction

CityFlow is a multi-agent reinforcement learning environment for large scale city traffic scenario.

Checkout these features!

• a microscopic traffic simulator which simulates the behavior of each vehicle, providing highest level detail of traffic evolution.
• support flexible definitions for road network and traffic flow
• provides friendly python interface for reinforcement learning
• Fast! Elaborately designed data structure and simulation algorithm with multithreading. Capable of simulating city-wide traffic. See the performance comparison with SUMO [2].

Performance comparison between CityFlow with different number of threads (1, 2, 4, 8) and SUMO. From small 1x1 grid roadnet to city-level 30x30 roadnet. Even faster when you need to interact with the simulator through python API.

See Quick Start to get started.

[1]	WWW 2019 Demo Paper

[2]	SUMO home page