Deep Reinforcement Learning-Based Tax and Economic Agents Policy Optimization Simulation Environment Analysis and Experiment 


Vol. 48,  No. 6, pp. 755-763, Jun.  2023
10.7840/kics.2023.48.6.755


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  Abstract

With the fourth industrial revolution, AI has been commercialized and continuously developed throughout society. However, the economic sector still faces challenges in applying AI due to a lack of data, various environments, and variables. To address real economic problems, it is necessary to identify and test various environmental and interactive factors among economic entities in the process of designing and testing economic activities and policies. However, economic data is lacking and it is difficult to create an environment for experimenting with actual policies. In this paper, we utilize the AI Economist, an AI-based economic simulation environment developed by the Salesforce team, to conduct experiments and analysis on tax and economic activity agent policy optimization based on deep reinforcement learning

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[IEEE Style]

J. Heo, Y. Choi, Y. Seok, Y. Lee, T. You, Y. Han, "Deep Reinforcement Learning-Based Tax and Economic Agents Policy Optimization Simulation Environment Analysis and Experiment," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 755-763, 2023. DOI: 10.7840/kics.2023.48.6.755.

[ACM Style]

Joo-Seong Heo, Yo-Han Choi, Yeong-Jun Seok, Yeonhee Lee, Taewan You, and Youn-Hee Han. 2023. Deep Reinforcement Learning-Based Tax and Economic Agents Policy Optimization Simulation Environment Analysis and Experiment. The Journal of Korean Institute of Communications and Information Sciences, 48, 6, (2023), 755-763. DOI: 10.7840/kics.2023.48.6.755.

[KICS Style]

Joo-Seong Heo, Yo-Han Choi, Yeong-Jun Seok, Yeonhee Lee, Taewan You, Youn-Hee Han, "Deep Reinforcement Learning-Based Tax and Economic Agents Policy Optimization Simulation Environment Analysis and Experiment," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 755-763, 6. 2023. (https://doi.org/10.7840/kics.2023.48.6.755)
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