论文标题
效率和公平性都是必不可少的：具有深入增强学习的广义交通信号控制器
Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning
论文作者
论文摘要
流量信号控制器在当今的交通系统中起着至关重要的作用。但是，当前大多数人都不足够灵活或自适应来生成最佳的流量时间表。在本文中，我们介绍了一种使用深厚的增强学习旨在优化交通流的学习策略的方法。我们的方法使用了一种新颖的奖励功能公式，同时考虑了效率和公平性。此外，我们提出了一种通用方法，可以找到提议的权益因素的界限，并介绍了自适应折现方法，该方法极大地稳定学习并有助于保持绿色光持续时间的高灵活性。对模拟和现实世界数据的实验评估表明，我们提出的算法在广泛的交通情况下实现了最先进的性能（以前由传统非学习方法持有）。
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.
