Shunting trains with deep reinforcement learning

Evertjan Peer, V. Menkovski, Y. Zhang, Wan-Jui Lee

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

24 Citaten (Scopus)
992 Downloads (Pure)

Samenvatting

The Train Unit Shunting Problem (TUSP) is a difficult sequential decision making problem faced by Dutch Railways (NS). Current heuristic solutions under study at NS fall short in accounting for uncertainty during plan execution and do not efficiently support replanning. Furthermore, the resulting plans lack consistency. We approach the TUSP by formulating it as a Markov Decision Process and develop an image-like state space representation that allows us to develop a Deep Reinforcement Learning (DRL) solution. The Deep Q-Network efficiently reduces the state space and develops an on-line strategy for the TUSP capable of dealing with uncertainty and delivering significantly more consistent solutions compared to approaches currently being developed by NS.
Originele taal-2Engels
Titel2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
UitgeverijIEEE-SMC
Pagina's3063-3068
Aantal pagina's6
ISBN van elektronische versie9781538666500
DOI's
StatusGepubliceerd - 2018
Evenement2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC2018 - Miyazaki, Japan
Duur: 7 okt. 201810 okt. 2018
http://www.smc2018.org/

Congres

Congres2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC2018
Verkorte titelSMC 2018
Land/RegioJapan
StadMiyazaki
Periode7/10/1810/10/18
Internet adres

Vingerafdruk

Duik in de onderzoeksthema's van 'Shunting trains with deep reinforcement learning'. Samen vormen ze een unieke vingerafdruk.

Citeer dit