Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.

A definição de parâmetros é uma importante etapa para a utilização de métodos de Aprendizado de Máquina. No entanto, pode ser altamente custoso definir esses valores de condições iniciais para cada aplicação. Assim, este trabalho tem como objetivo propor um sistema de Aprendizado de Máquina Automatizado para ajuste de parâmetros. Nesta linha, foi desenvolvido um método de Aprendizado por Reforço Automatizado aplicado ao Problema do Caixeiro Viajante. O sistema proposto ajustou através da Metodologia de Superfície de Resposta dois parâmetros (taxa de aprendizado e fator de desconto) do algoritmo Q-learning. Os resultados revelaram que os valores ajustados pelo método proposto alcançaram, em geral, as melhores soluções, em comparação com a adoção de parâmetros da literatura.

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.

Título: AutoML e transferência de aprendizado por reforço entre problemas de otimização combinatória Autores: Gleice Kelly Barbosa Souza, Andre Luiz Carvalho Ottoni Resumo: O Aprendizado por Reforço (AR) e uma das linhas do Aprendizado de Máquina e que pode ser Read More ...