  Traffic signal controllers play an essential role in today's traffic system.
However, the majority of them currently is not sufficiently flexible or
adaptive to generate optimal traffic schedules. In this paper we present an
approach to learning policies for signal controllers using deep reinforcement
learning aiming for optimized traffic flow. Our method uses a novel formulation
of the reward function that simultaneously considers efficiency and equity. We
furthermore present a general approach to find the bound for the proposed
equity factor and we introduce the adaptive discounting approach that greatly
stabilizes learning and helps to maintain a high flexibility of green light
duration. The experimental evaluations on both simulated and real-world data
demonstrate that our proposed algorithm achieves state-of-the-art performance
(previously held by traditional non-learning methods) on a wide range of
traffic situations.
