Open AccessApplication of Reinforcement Learning to Optimize Business Processes in the Bank
Author(s) -
Andrey A. Bugaenko
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i6.3200
Subject(s) - reinforcement learning , computer science , reinforcement , principle of maximum entropy , hyperparameter , business process , artificial intelligence , entropy (arrow of time) , genetic algorithm , process (computing) , machine learning , mathematical optimization , mathematics , work in process , operations management , engineering , physics , structural engineering , quantum mechanics , operating system
This article describes the application of reinforcement learning (q-learning, genetic algorithm, cross-entropy) to define the optimal structure of business processes in the bank. It describes the principle of creation of the environment, loss, and reward. Setting of hyperparameters for each method is considered in depth. Besides, it offers the variant of calculation of the maximum potential for saving, which can be arrived at through the business process optimization.