Post #519

Reinforcement learning for combinatorial optimization: A survey Our work that surveys recent RL methods for solving combinatorial optimization problems is accepted at Computers & Operations Research journal. This is very active field right now and it shows a lot of promise. Traditionally, NP-hard problems such as Traveling Salesman Problem were solved by algorithms, that were designed specifically for each problem. With RL, it's possible to extend the toolbox by learning a function on available data. I really hope that in 10 years from now using ML approaches for combinatorial problems will be a commonplace.