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Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies

Autor: Yu, Jiajie, Wang, Yuhong, Ma, Wei

Bus holding control is a widely-adopted strategy for maintaining stability and improving the operational efficiency of bus systems. Traditional model-based methods often face challenges with the low accuracy of bus state prediction and passenger dema

Externí odkaz: http://arxiv.org/abs/2410.10212

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Online Prediction-Assisted Safe Reinforcement Learning for Electric Vehicle Charging Station Recommendation in Dynamically Coupled Transportation-Power Systems

Autor: Liao, Qionghua, Li, Guilong, Yu, Jiajie, Gu, Ziyuan, Ma, Wei

With the proliferation of electric vehicles (EVs), the transportation network and power grid become increasingly interdependent and coupled via charging stations. The concomitant growth in charging demand has posed challenges for both networks, highl

Externí odkaz: http://arxiv.org/abs/2407.20679

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Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach

Autor: Yu, Jiajie, Laharotte, Pierre-Antoine, Han, Yu, Ma, Wei, Leclercq, Ludovic

Perimeter Control (PC) strategies have been proposed to address urban road network control in oversaturated situations by regulating the transfer flow of the Protected Network (PN) based on the Macroscopic Fundamental Diagram (MFD). The uniform meter

Externí odkaz: http://arxiv.org/abs/2308.12985

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A Heuristic Autonomous Exploration Method Based on Environmental Information Gain During Quadrotor Flight

Autor: Zhang, Tong, Yu, Jiajie, Li, Jiaqi, Pang, Minghui

Autonomous exploration is a widely studied fundamental application in the field of quadrotors, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, which is one of

Externí odkaz: http://arxiv.org/abs/2302.10419

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