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Optimising a Real-time Scheduler for Railway Lines using Policy Search
Journal
ALA 2020 - Adaptive and Learning Agents Workshop at AAMAS 2020
Date Issued
2020-01-01
Author(s)
Prasad, Rohit
Kalyanakrishnan, Shivaram
Khadilkar, Harshad
Abstract
This paper describes a policy search approach for railway scheduling using the covariance matrix adaptation evolution strategy (CMA-ES). The goal is to define arrival/departure times and track allocations for all trains such that the resource and operating constraints of the railway line are satisfied, and priority-weighted train departure delay is minimised. The proposed approach is scalable in the sense that (i) the optimised policy can be applied to an arbitrarily long railway line, independent of the number of trains, tracks, and stations, and (ii) the on-line implementation is computationally light enough to be applied in real-time. Our experiments show that policies computed with CMA-ES are able to produce solutions with lower priority-weighted delay than heuristics and reinforcement learning (RL) algorithms reported in literature, on synthetic examples as well as actual railway line data from portions of the Indian Railway network.
Subjects