层次聚类在深度强化学习控制交通网络中的应用

Fady Taher, A. Elmahalawy, A. Shouman, A. El-Sayed
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引用次数: 0

摘要

交通拥堵是影响全球城市的一个关键问题,随着车辆数量的显著增加,交通拥堵只会变得越来越严重。交通信号控制器被认为是控制交通的最重要的机制,特别是在十字路口,机器学习领域引入了先进的技术,可以为交通控制技术提供更多的灵活性和适应性。有效的交通控制器可以使用强化学习(RL)方法来设计,但RL方法的主要问题是,状态和行动空间的指数增长以及对协调的需求。我们使用渥太华市65个十字路口的真实交通数据来构建我们的模拟,并表明,使用分层技术聚类网络在显著减少状态对和提高整体交通性能方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Hierarchal Clusters on Deep Reinforcement Learning Controlled Traffic Network
Traffic congestions is a crucial problem affectingcities around the globe and they are only getting worse as thenumber of vehicles tends to increase significantly. Traffic signalcontrollers are considered as the most important mechanism tocontrol traffic, specifically at intersections, the field of MachineLearning introduces advanced techniques which can be appliedto provide more flexibility and adaptiveness to traffic controltechniques. Efficient traffic controllers can be designed using areinforcement learning (RL) approach but major problems offollowing RL approach are, exponential growth in the state andaction spaces and the need for coordination. We use real trafficdata of 65 intersection of the city of Ottawa to build oursimulations and show that, clustering the network usinghierarchal techniques has a great potential in reducing the stateactionpair significantly and enhance overall trafficperformance.
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