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B. Kovári, I.F. Lovetei, Sz. Aradi, T. Bécsi, "Multi-Agent Deep Reinforcement Learning (MADRL) for Solving Real-Time Railway Rescheduling Problem", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 23.5, 2022, doi:10.4203/ccc.1.23.5

Keywords: Rescheduling, Railway Traffic, Multi-Agent Deep Reinforcement Learning, Markov Decision Process.

Abstract

The real-time railway rescheduling problem is a challenging task since several factors have to be considered when a train deviates from the initial timetable. Nowadays, the problem is solved by human operators, which is safe but not optimal. This paper proposes a novel state representation for the introduced control problem that enables the efficient utilization of Multi-Agent Deep Reinforcement Learning. To support our claim, a proof of concept network is implemented, and the performance of the trained agent is evaluated. The results show that our approach enables fast convergence and excellent performance, while the representation has the potential for solving much more complex networks.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 1 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 23.5 Multi-Agent Deep Reinforcement Learning (MADRL) for Solving Real-Time Railway Rescheduling Problem B. Kovári, I.F. Lovetei, Sz. Aradi and T. Bécsi Budapest University of Technology and Economics, Budapest, Hungary doi:10.4203/ccc.1.23.5 Full Bibliographic Reference for this paper B. Kovári, I.F. Lovetei, Sz. Aradi, T. Bécsi, "Multi-Agent Deep Reinforcement Learning (MADRL) for Solving Real-Time Railway Rescheduling Problem", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 23.5, 2022, doi:10.4203/ccc.1.23.5 Keywords: Rescheduling, Railway Traffic, Multi-Agent Deep Reinforcement Learning, Markov Decision Process. Abstract The real-time railway rescheduling problem is a challenging task since several factors have to be considered when a train deviates from the initial timetable. Nowadays, the problem is solved by human operators, which is safe but not optimal. This paper proposes a novel state representation for the introduced control problem that enables the efficient utilization of Multi-Agent Deep Reinforcement Learning. To support our claim, a proof of concept network is implemented, and the performance of the trained agent is evaluated. The results show that our approach enables fast convergence and excellent performance, while the representation has the potential for solving much more complex networks. download the full-text of this paper (PDF, 6 pages, 701 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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