This paper investigates the reliable shortest path (RSP) planning problem from the reinforcement learning perspective. Different from canonical path planning methods, which require at least the first- order statistic (mean) and second-order statistic (variance) information of travel time distribution, we target at the RSP planning problem without the assumption of knowing any travel time distribution characteristic beforehand, and propose a cascaded temporal difference learning (CTD) method, which simultaneously estimates the mean and variance of the executing path and thereby gradually makes improvements through the generalized policy iteration (GPI) scheme, as the ego vehicle interacts with the environment. Extensive simulation results demonstrate the applicability of the proposed method for RSP learning in various transportation networks.
CTD: Cascaded Temporal Difference Learning for the Mean-Standard Deviation Shortest Path Problem
IEEE Transactions on Intelligent Transportation Systems ; 23 , 8 ; 10868-10886
2022-08-01
7097605 byte
Article (Journal)
Electronic Resource
English
A generalized Benders decomposition approach for the mean-standard deviation shortest path problem
Taylor & Francis Verlag | 2023
|European Patent Office | 2024
|The Variance-Constrained Shortest Path Problem
Online Contents | 1994
|Shortest path problem with arc failure scenarios
Online Contents | 2017
|