Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode

Author:

Jinyang Jiang, Xiaotian Liu, Tao Ren, Qinghao Wang, Yi Zheng, Yufu Du, Yijie Peng, Cheng Zhang

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Machine Learning (cs.LG)

journal:

date:

2024-03-01 00:00:00

Abstract

We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation. This DRL approach has the potential to lead to a large management model based on certain transformer neural network structures, resulting in an artificial general intelligence paradigm for various management tasks. Traditional methods have limitations for solving complex real-world problems, and we demonstrate how DRL can surpass existing heuristic approaches for solving management tasks. We aim to solve the problems in a unified framework, considering the interconnections between different tasks. Central to our methodology is the development of a foundational decision model coordinating decisions across the different domains through generative decision-making. Our experimental results affirm the effectiveness of our DRL-based framework in complex and dynamic business environments. This work opens new pathways for the application of DRL in management problems, highlighting its potential to revolutionize traditional business management.

PDF: Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode.pdf