首页|Investigators from Changchun University of Technology Zero in on Intelligent Systems (Load Balancing of Multi-agv Road Network Based On Improved Q-learning Algorithm and Macroscopic Fundamental Diagram)
Investigators from Changchun University of Technology Zero in on Intelligent Systems (Load Balancing of Multi-agv Road Network Based On Improved Q-learning Algorithm and Macroscopic Fundamental Diagram)
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Springer Nature
Investigators publish new report on Machine Learning - Intelligent Systems. According to news reporting originating from Changchun, People's Republic of China, by NewsRx correspondents, research stated, “To address the challenges of traffic congestion and suboptimal operational efficiency in the context of large-scale applications like production plants and warehouses that utilize multiple automatic guided vehicles (multi-AGVs), this article proposed using an Improved Q-learning (IQL) algorithm and Macroscopic Fundamental Diagram (MFD) for the purposes of load balancing and congestion discrimination on road networks. Traditional Q-learning converges slowly, which is why we have proposed the use of an updated Q value of the previous iteration step as the maximum Q value of the next state to reduce the number of Q value comparisons and improve the algorithm's convergence speed.” Financial support for this research came from Jilin Province Major Science and Technology Special Project “Research on Repeat Positioning Accuracy Technology of AGV.”
ChangchunPeople’s Republic of ChinaAsiaIntelligent SystemsMachine LearningAlgorithmsTraffic CongestionTransportationChangchun University of Tech- nology
NETL
NSTL
Springer Nature
