Výsledky vyhledávání - "Sarwar, Tabinda"

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Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems

Autor: Sultana, Nasrin, Chan, Jeffrey, Sarwar, Tabinda, Qin, A. K.

Model-free deep-reinforcement-based learning algorithms have been applied to a range of COPs~\cite{bello2016neural}~\cite{kool2018attention}~\cite{nazari2018reinforcement}. However, these approaches suffer from two key challenges when applied to comb

Externí odkaz: http://arxiv.org/abs/2205.15656

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Learning Enhanced Optimisation for Routing Problems

Autor: Sultana, Nasrin, Chan, Jeffrey, Sarwar, Tabinda, Abbasi, Babak, Qin, A. K.

Deep learning approaches have shown promising results in solving routing problems. However, there is still a substantial gap in solution quality between machine learning and operations research algorithms. Recently, another line of research has been

Externí odkaz: http://arxiv.org/abs/2109.08345

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Learning Vehicle Routing Problems using Policy Optimisation

Autor: Sultana, Nasrin, Chan, Jeffrey, Qin, A. K., Sarwar, Tabinda

Deep reinforcement learning (DRL) has been used to learn effective heuristics for solving complex combinatorial optimisation problem via policy networks and have demonstrated promising performance. Existing works have focused on solving (vehicle) rou

Externí odkaz: http://arxiv.org/abs/2012.13269

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Learning to Optimise General TSP Instances

Autor: Sultana, Nasrin, Chan, Jeffrey, Qin, A. K., Sarwar, Tabinda

The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving efforts assist in learning how to optimise future optimisation instances. I

Externí odkaz: http://arxiv.org/abs/2010.12214

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