Experiments on Combinatorial Optimization with Reinforcement Learning Using Deep Learning and Monte Carlo Tree Search and a Consideration of Frame Problem

Abstract

Reinforcement learning using deep learning and Monte Carlo tree search has been reported to be extremely effective as an artificial intelligence algorithm that is used in AlphaZero etc. and is widely applicable to various games. Since this method is essentially an algorithm that solves the search problem efficiently, it is possible to solve a general combination optimization problem as well as a game. Therefore, in order to deepen the understanding of this method, experiments were applied to combinatorial optimization problem, and the results are reported. The relationship between this method and the frame problem also be described.