Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
<|reference_start|>Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode: 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.<|reference_end|>
@article{jiang2024deep,
    title={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 and Cheng Zhang},
    journal={arXiv preprint arXiv:2403.00318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00318},
    primaryClass={cs.AI cs.LG}
}