Deep Reinforcement Learning for Multiobjective Optimization

Kaiwen Li

doi:10.1109/TCYB.2020.2977661

Return

Deep Reinforcement Learning for Multiobjective Optimization

2021-06-01

197

Kaiwen Li

张涛 (Tao Zhang)

王锐 (Rui Wang) *

DOI：10.1109/TCYB.2020.2977661

Original

Save

View PDF

Abstract

En 中文

This article proposes an end-to-end framework for solving multiobjective optimization problems (MOPs) using deep reinforcement learning (DRL), that we call DRL-based multiobjective optimization algorithm (DRL-MOA). The idea of decomposition is adopted to decompose the MOP into a set of scalar optimization subproblems. Then, each subproblem is modeled as a neural network. Model parameters of all the subproblems are optimized collaboratively according to a neighborhood-based parameter-transfer strategy and the DRL training algorithm. Pareto-optimal solutions can be directly obtained through the trained neural-network models. Specifically, the multiobjective traveling salesman problem (MOTSP) is solved in this article using the DRL-MOA method by modeling the subproblem as a Pointer Network. Extensive experiments have been conducted to study the DRL-MOA and various benchmark methods are compared with it. It is found that once the trained model is available, it can scale to newly encountered problems with no need for retraining the model. The solutions can be directly obtained by a simple forward calculation of the neural network; thereby, no iteration is required and the MOP can be always solved in a reasonable time. The proposed method provides a new way of solving the MOP by means of DRL. It has shown a set of new characteristics, for example, strong generalization ability and fast solving speed in comparison with the existing methods for multiobjective optimizations. The experimental results show the effectiveness and competitiveness of the proposed method in terms of model performance and running time.

Keywords:

Optimization

Training

Urban areas

Neural networks

Reinforcement learning

Traveling salesman problems

Modeling

Deep reinforcement learning (DRL)

multiobjective optimization

Pointer Network

traveling salesman problem

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.