A multi-stage group decision model based on improved Q-learning

被引：0
|
作者：
Zhang F. [1 ,2 ]
Liu L.-Y. [1 ,2 ]
Guo X.-X. [1 ,2 ]
机构：
[1] College of Mathematics and Information Science, Hebei University, Baoding
[2] Hebei Key Laboratory of Machine Learning and Computational Intelligence, Baoding
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 09期
关键词：
Group consensus; Group decision making; Multi-stage group decision; Q-learning; Reinforcement learning; Uncertainty;
D O I：
10.13195/j.kzyjc.2018.0082
中图分类号：
学科分类号：
摘要：
The multi-stage group decision making problem is a typical sequential group decision making problem. It is normally utilized to find the optimal solution to the group decision problems in discrete deterministic environment. However, the real life environments faced by decision-makers are usually full of uncertainty, even unknown environments (with unknown state transition matrix). Therefore, it is essential for the decision-makers to obtain more information by interacting with the environment dynamically to achieve an optimal decision strategy with high consensus degree. Due to the advantage of reinforcement learning in handling the sequential decision-making problems, the classical reinforcement learning algorithm (Q-learning) is improved to discover the optimal solution of multi-stage group decision making problems under uncertain environment. Additionally, a theorem is proposed to show that the optimal group decision obtained by using the improved Q-learning algorithm is the group decision with the highest degree of group consensus. Finally, an illustrative example is presented to verify the rationality and feasibility of the proposed algorithm. © 2019, Editorial Office of Control and Decision. All right reserved.
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页码：1917 / 1922
页数：5
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