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Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks

机译：车辆网络中城市交通灯控制多功能深度增强学习

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摘要

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement learning (MARL). Most existing MARL studies are based on traditional $Q$-learning, but unstable environment leads to poor learning in the complicated and dynamic traffic scenarios. In this paper, we propose a novel multi-agent recurrent deep deterministic policy gradient (MARDDPG) algorithm based on deep deterministic policy gradient (DDPG) algorithm for traffic light control (TLC) in vehiclar networks. Specifically, the centralized learning in each critic network enables each agent to estimate the policies of other agents in the decision-making process and each agent can coordinate with each other, alleviating the problem of poor learning performance caused by environmental instability. The decentralized execution enables each agent to make decisions independently. We share parameters in actor networks to speed up the training process and reduce the memory footprint. The addition of LSTM is beneficial to alleviate the instability of the environment caused by partial observable state. We utilize surveillance cameras and vehicular networks to collect status information for each intersection. Unlike previous work, we have not only considered the vehicle but also considered the pedestrians waiting to pass through the intersection. Moreover, we also set different priorities for buses and ordinary vehicles. The experimental results in a vehicular network show that our method can run stably in various scenarios and coordinate multiple intersections, which significantly reduces vehicle congestion and pedestrian congestion.
机译：随着城市交通状况多样化，复杂，施加钢筋，减少交通拥堵成为炎热和有前途的主题之一。特别是，如何协调多个交叉路口的交通光控制器是多智能体增强学习（MARL）的关键挑战。大多数现有的Marl研究都基于传统的<内联公式XMLNS：MML =“http://www.w3.org/1998/math/mathml”xmlns：xlink =“http://www.w3.org/1999/xlink”> $ Q $ - 学习，但不稳定的环境导致复杂和动态的交通方案中的学习差。在本文中，我们提出了一种基于车辆网络中的交通光控制（TLC）的深度确定性政策梯度（DDPG）算法的新型多种代理经常性深度确定性政策梯度（MARDDPG）算法。具体而言，每个批评网络中的集中式学习使每个代理能够在决策过程中估计其他代理的政策，并且每个代理人可以相互协调，减轻了由环境不稳定引起的学习性能差的问题。分散的执行使每个特工能够独立做出决策。我们在演员网络中共享参数，加快培训过程并减少内存占用。添加LSTM是有益的，可以缓解部分可观察状态引起的环境的不稳定性。我们利用监控摄像机和车辆网络来收集每个交叉路口的地位信息。与以前的工作不同，我们不仅考虑了车辆，还考虑到行人等待通过交叉路口。此外，我们还为公共汽车和普通车辆设置了不同的优先事项。在车辆网络中的实验结果表明，我们的方法可以在各种场景中稳定地运行，并坐标多个交叉点，这显着降低了车辆拥塞和行人拥堵。

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  • 来源
    《IEEE Transactions on Vehicular Technology》 |2020年第8期|8243-8256|共14页
  • 作者

    Tong Wu; Pan Zhou; Kai Liu; Yali Yuan; Xiumin Wang; Huawei Huang; Dapeng Oliver Wu;

  • 作者单位

    School of Electrical Information and Communication Engineering Huazhong University of Science & Technology Wuhan China;

    Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science & Technology Wuhan China;

    College of Computer Science Chongqing University Chongqing China;

    Institute of Computer Science Göttingen University Göttingen Germany;

    School of Computer Science and Engineering South China University of Technology Guangzhou China;

    School of Data and Computer Science Sun Yat-Sen University Guangzhou China;

    Department of Electrical and Computer Engineering University of Florida Gainesville FL USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Reinforcement learning; Heuristic algorithms; Computer science; Vehicle dynamics; Training; Process control; Internet of Things;

    机译：强化学习;启发式算法;计算机科学;车辆动态;培训;过程控制;事物互联网;

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