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

TL;DR

This work demonstrates how DRL can surpass existing heuristic approaches for solving management tasks, and opens new pathways for the application of DRL in management problems, highlighting its potential to revolutionize traditional business management.

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.

Figures (4)

• Figure 1: Comparison of reward under different scenarios.
• Figure 2: Comparison of reward KDEs under different scenarios.
• Figure 3: The overall framework of LMM based on the foundation model.
• Figure 4: Performance of LMM based on the foundation model.