Global search in combinatorial optimization using reinforcement learning algorithms

This paper presents two approaches that address the problems of the local character of the search and imprecise state representation of reinforcement learning (RL) algorithms for solving combinatorial optimization problems. The first, Bayesian, approach aims to capture solution parameter interdependencies. The second approach combines local information as encoded by typical RL schemes and global information as contained in a population of search agents. The effectiveness of these approaches is demonstrated on the quadratic assignment problem. Competitive results with the RL-agent approach suggest that it can be used as a basis for global optimization techniques.