Welcome to the InfoSci Platform
Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning

Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning

Chunmao Jiang
Copyright: © 2024 |Pages: 22
ISBN13: 9798369315828|ISBN13 Softcover: 9798369363911|EISBN13: 9798369315835
DOI: 10.4018/979-8-3693-1582-8.ch006
Cite Chapter Cite Chapter

MLA

Jiang, Chunmao. "Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning." Big Data Quantification for Complex Decision-Making, edited by Chao Zhang and Wentao Li, IGI Global, 2024, pp. 147-168. https://doi.org/10.4018/979-8-3693-1582-8.ch006

APA

Jiang, C. (2024). Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning. In C. Zhang & W. Li (Eds.), Big Data Quantification for Complex Decision-Making (pp. 147-168). IGI Global. https://doi.org/10.4018/979-8-3693-1582-8.ch006

Chicago

Jiang, Chunmao. "Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning." In Big Data Quantification for Complex Decision-Making, edited by Chao Zhang and Wentao Li, 147-168. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1582-8.ch006

Export Reference

Mendeley
Favorite

Abstract

In this chapter, we enhance the trisecting-acting-outcome (TAO) model of three-way decision-making (3WD) with a novel approach for strategy selection and outcome prediction using Q-learning in reinforcement learning. We reinterpret the changes in tripartition and actions in the TAO model as states and actions in reinforcement learning, respectively. The reward is quantified using cumulative prospect theory, and the Q-learning algorithm iteratively determines action sets that achieve target rewards efficiently. This method offers a cost-effective and psychologically attuned action set for predicting the utility in change-based 3WD, demonstrated through a practical example.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.