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• W4313125087 abstract "Bus system is a critical component of sustainable urban transportation. However, the operation of a bus fleet is unstable in nature, and bus bunching has become a common phenomenon that undermines the efficiency and reliability of bus systems. Recently research has demonstrated the promising application of multi-agent reinforcement learning (MARL) to achieve efficient vehicle holding control to avoid bus bunching. However, existing studies essentially overlook the robustness issue resulting from various events, perturbations and anomalies in a transit system, which is of utmost importance when transferring the models for real-world deployment/application. In this study, we integrate implicit quantile network and meta-learning to develop a distributional MARL framework -- IQNC-M -- to learn continuous control. The proposed IQNC-M framework achieves efficient and reliable control decisions through better handling various uncertainties/events in real-time transit operations. Specifically, we introduce an interpretable meta-learning module to incorporate global information into the distributional MARL framework, which is an effective solution to circumvent the credit assignment issue in the transit system. In addition, we design a specific learning procedure to train each agent within the framework to pursue a robust control policy. We develop simulation environments based on real-world bus services and passenger demand data and evaluate the proposed framework against both traditional holding control models and state-of-the-art MARL models. Our results show that the proposed IQNC-M framework can effectively handle the various extreme events, such as traffic state perturbations, service interruptions, and demand surges, thus improving both efficiency and reliability of the system." @default.
• W4313125087 created "2023-01-06" @default.
• W4313125087 creator A5044763915 @default.
• W4313125087 creator A5058941074 @default.
• W4313125087 date "2023-04-01" @default.
• W4313125087 modified "2023-09-27" @default.
• W4313125087 title "Robust Dynamic Bus Control: a Distributional Multi-Agent Reinforcement Learning Approach" @default.
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• W4313125087 doi "https://doi.org/10.1109/tits.2022.3229527" @default.
• W4313125087 hasPublicationYear "2023" @default.
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