Robot

Algorithm

Pure mathematics

Construct (python library)

Statistics

Theoretical computer science

Field (mathematics)

Operating system

Computer science

Hyper-heuristic

Mathematical optimization

Robot learning

Timeline

Reinforcement learning

Graph

Mathematics

Mobile robot

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Programming language

Optimization problem

Machine learning

Heuristics

Combinatorial optimization

Computers & Operations Research

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.

Reinforcement learning for combinatorial optimization: A survey