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Titel:

Hybrid Residual Multiexpert Reinforcement Learning for Spatial Scheduling of High-Density Parking Lots

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Hou, Jing; Chen, Guang; Li, Zhijun; He, Wei; Gu, Shangding; Knoll, Alois; Jiang, Changjun
Abstract:
Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the growing parking challenges in large cities, high-density vehicle spatial scheduling is one of the potential solutions. Stack-based parking lots utilize parking robots to densely park vehicles in the vertical stacks like container stacking, which greatly reduces the aisle area in the parking lot, but requires complex schedul...     »
Zeitschriftentitel:
IEEE transactions on cybernetics
Jahr:
2023
Band / Volume:
PP
Monat:
October
Volltext / DOI:
doi:10.1109/tcyb.2023.3312647
WWW:
https://doi.org/10.1109/TCYB.2023.3312647
Print-ISSN:
2168-2267
 BibTeX