Deep Reinforcement Learning for Solving Management Problems: Towards A
  Large Management Mode
Jinyang Jiang Xiaotian Liu Tao Ren Qinghao Wang Yi Zheng Yufu Du Yijie Peng Cheng Zhang
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.