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The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation systems decision support tools. Reinforcement learning, an artificial intelligence approach undergoing development in the machinelearning community, offers key advantages in this regard. The ability of a control agent to learn relationships between control actions and their effect on the environment while pursuing a goal is a distinct improvement over prespecified modelsdoi:10.1061/(asce)0733-947x(2003)129:3(278) fatcat:ocpavsaih5h57l2hqz2kbjslpy