On a Successful Application of Multi-Agent Reinforcement Learning to Operations Research Benchmarks

In this paper, we suggest and analyze the use of approximate reinforcement learning techniques for a new category of challenging benchmark problems from the field of operations research. We demonstrate that interpreting and solving the task of job-shop scheduling as a multi-agent learning problem is beneficial for obtaining near-optimal solutions and can very well compete with alternative solution approaches. The evaluation of our algorithms focuses on numerous established operations research benchmark problems