Reinforcement learning for combinatorial optimization: A survey

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.

Automated summary

The study explores the recent advancements in applying reinforcement learning frameworks to hard combinatorial problems, showcasing their potential in training agents to automate the search for heuristics. It also compares RL methods with traditional algorithms, demonstrating that RL models could be a promising direction for solving combinatorial problems.