Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916143679143936 |
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| author | Jiang, Jinyang Liu, Xiaotian Ren, Tao Wang, Qinghao Zheng, Yi Du, Yufu Peng, Yijie Zhang, Cheng |
| author_facet | Jiang, Jinyang Liu, Xiaotian Ren, Tao Wang, Qinghao Zheng, Yi Du, Yufu Peng, Yijie Zhang, Cheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00318 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode Jiang, Jinyang Liu, Xiaotian Ren, Tao Wang, Qinghao Zheng, Yi Du, Yufu Peng, Yijie Zhang, Cheng Artificial Intelligence Machine Learning 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. |
| title | Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.00318 |