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Reinforcement learning for combinatorial optimization: A survey - 2021

Reinforcement Learning For Combinatorial Optimization: A Survey

Research Area:  Machine Learning


Many traditional algorithms for solving combinatorial optimization problems involve using hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed by domain experts and may often be suboptimal due to the hard nature of the problems. Reinforcement learning (RL) proposes a good alternative to automate the search of these heuristics by training an agent in a supervised or self-supervised manner. In this survey, we explore the recent advancements of applying RL frameworks to hard combinatorial problems. Our survey provides the necessary background for operations research and machine learning communities and showcases the works that are moving the field forward. We juxtapose recently proposed RL methods, laying out the timeline of the improvements for each problem, as well as we make a comparison with traditional algorithms, indicating that RL models can become a promising direction for solving combinatorial problems.


Author(s) Name:  Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev

Journal name:  Computers & Operations Research

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.cor.2021.105400

Volume Information:  Volume 134, October 2021, 105400