AutoRL-Sim: Automated Reinforcement Learning Simulator for Combinatorial Optimization Problems

Abstract

Reinforcement learning is a crucial area of machine learning, with a wide range of applications. To conduct experiments in this research field, it is necessary to define the algorithms and parameters to be applied. However, this task can be complex because of the variety of possible configurations. In this sense, the adoption of AutoRL systems can automate the selection of these configurations, simplifying the experimental process. In this context, this work aims to propose a simulation environment for combinatorial optimization problems using AutoRL. The AutoRL-Sim includes several experimentation modules that cover studies on the symmetric traveling salesman problem, the asymmetric traveling salesman problem, and the sequential ordering problem. Furthermore, parameter optimization is performed using response surface models. The AutoRL-Sim simulator allows users to conduct experiments in a more practical way, without the need to worry about implementation. Additionally, they have the ability to analyze post-experiment data or save them for future analysis.