翻译模板用于城市交通灯控制的增强型多主体多目标增强学习系统—朱文瑾(博士)翻译.doc

翻译：朱文瑾 学号：1212401007 2013年7月 2012 11th International Conference on Machine Learning and Applications 2012 第 11 届国际大会机器学习和应用 Enhanced Multiagent Multi-Objective Reinforcement Learning for Urban Traffic Light Control 用于城市交通灯控制的增强型多主体多目标增强学习系统 Mohamed A. Khamis?, Student Member, IEEE, and Walid Gomaa?,? ?Department of Computer Science and Engineering Egypt-Japan University of Science and Technology (E-JUST) Alexandria, Egypt Email: {mohamed.khamis, walid.gomaa}@.eg Abstract—Traffic light control is one of the major problems in urban areas. This is due to the increasing number of vehicles and the high dynamics of the traffic network. Ordinary methods for traffic light control cause high rate of accidents, waste in time, and affect the environment negatively due to the high rates of fuel consumption. In this paper, we develop an enhanced version of our multiagent multi-objective traffic light control system that is based on a Reinforcement Learning (RL) approach. As a testbed framework for our traffic light controller, we use the open source Green Light District (GLD) vehicle traffic simulator. We analyze and fix some implementation problems in GLD that emerged when applying a more realistic continuous time acceleration model. We propose a new cooperation method between the neighboring traffic light agent controllers using specific learning and exploration rates. Our enhanced traffic light controller minimizes the trip time in major arteries and increases safety in residential areas. In addition, our traffic light controller satisfies green waves for platoons traveling in major arteries and considers as well the traffic environmental impact by keeping the vehicles speeds within the desirable thresholds for lowest fuel consumption. In order to evaluate the enhancements and new methods proposed in this paper, we have added new performance indices to GLD. 摘要：交通灯控制是城市的主要问题之一。这都要归功于不断增长的车辆数量和交通网络的高动态性。传统的交通灯控制模式在这样的情况下会导致高交通事故率、时间的浪费，并且由于能源的浪费对环境造成负面影响。在本文中，我们开发了一种基于增强学习（R