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A multi-objective multi-agent framework for traffic light control
KTH, School of Architecture and the Built Environment (ABE), Transport Science.ORCID iD: 0000-0002-1375-9054
KTH, School of Architecture and the Built Environment (ABE), Transport Science.
2017 (English)In: 2017 11TH ASIAN CONTROL CONFERENCE (ASCC), IEEE , 2017, p. 1199-1204Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a multi-objective multi-agent framework for traffic light control. In particular, each agent in the proposed framework applies a multi-objective Markov decision process. For intelligent control, a reinforcement learning (RL) algorithm is enhanced with multiple-step backups and a function approximation approach to build the agent's knowledge. Moreover, a thresholded lexicographic ordering (TLO) action policy is integrated with the enhanced RL algorithm to solve the multi-objective control problem, which is reformulated by a constrained Markov decision process. A case study of three intersections is carried out and demonstrates the approach with a conventional stage-based phasing strategy using traffic simulation. The simulation experiments elaborate the benefits brought by MAMOD-TL system compared with optimized fixed-time controllers. More importantly, the Pareto optimality is approximately obtained by setting different control parameters for TLO action policy, which can be considered as a performance metric for decision makers.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1199-1204
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-225239ISI: 000426957300209OAI: oai:DiVA.org:kth-225239DiVA, id: diva2:1194590
Conference
2017 11TH ASIAN CONTROL CONFERENCE (ASCC)
Note

QC 20180403

Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2018-04-03Bibliographically approved

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Jin, JunchenMa, Xiaoliang

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