This talk 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 priorityweighted 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.

Optimising a Real-Time Scheduler for Railway Lines using Policy Search

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Shivaram Kalyanakrishnan

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Optimising a Real-Time Scheduler for Railway Lines using Policy Search

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