SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem

• Main Authors: GUO, Honglian, HE, Zhi, SHENG, Wenda, CAO, Zhiguang, ZHOU, Yingjie, GAO, Weinan
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2024
• 主題: Gaussian distribution; Generalized actor critic; Navigation; Optimization; Real-time systems; Reliability; Routing; sample efficiency; stochastic on-time arrival (SOTA); Transportation; variance reduction; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/8704; https://ink.library.smu.edu.sg/context/sis_research/article/9707/viewcontent/T_ITS_SEGAC_final.pdf
• 機構: Singapore Management University

This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the...