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

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

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...

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Bibliographic Details
Main Authors: GUO, Honglian, HE, Zhi, SHENG, Wenda, CAO, Zhiguang, ZHOU, Yingjie, GAO, Weinan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
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
Online Access: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
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Institution: Singapore Management University
Language: English

Internet

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

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