Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913106086592512 |
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| author | Guo, Honglei Zhao, Yuhan Li, Yexin |
| author_facet | Guo, Honglei Zhao, Yuhan Li, Yexin |
| contents | Reinforcement learning has increasingly been applied to economic decision-making, including taxation, public spending, and labor supply. However, existing RL-based economic models typically consider only a single government-household group, overlooking strategic interactions among competing governments. To address this limitation, we formulate taxation as a hierarchical multi-group game. Within each group, the government and households form a leader--follower game, while governments compete across groups through strategic fiscal policies. This coupled structure is difficult to solve using standard multi-agent reinforcement learning (MARL) methods. We therefore propose a bilevel MARL framework with \textit{Curriculum Learning} and a \textit{Closed-Loop Sequential Update} mechanism to improve training stability and convergence. We instantiate the framework in a taxation simulation environment grounded in classical economic models, supporting the evaluation of taxation policies under inter-group competition. Experiments show that the proposed method learns stable and sustainable tax policies. Compared with a two-group baseline without the proposed mechanisms, our approach avoids premature game collapse, extends the effective game duration by 60.92\%, and reduces GDP disparities among governments by 44.12\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04741 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game Guo, Honglei Zhao, Yuhan Li, Yexin Multiagent Systems Reinforcement learning has increasingly been applied to economic decision-making, including taxation, public spending, and labor supply. However, existing RL-based economic models typically consider only a single government-household group, overlooking strategic interactions among competing governments. To address this limitation, we formulate taxation as a hierarchical multi-group game. Within each group, the government and households form a leader--follower game, while governments compete across groups through strategic fiscal policies. This coupled structure is difficult to solve using standard multi-agent reinforcement learning (MARL) methods. We therefore propose a bilevel MARL framework with \textit{Curriculum Learning} and a \textit{Closed-Loop Sequential Update} mechanism to improve training stability and convergence. We instantiate the framework in a taxation simulation environment grounded in classical economic models, supporting the evaluation of taxation policies under inter-group competition. Experiments show that the proposed method learns stable and sustainable tax policies. Compared with a two-group baseline without the proposed mechanisms, our approach avoids premature game collapse, extends the effective game duration by 60.92\%, and reduces GDP disparities among governments by 44.12\%. |
| title | Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2605.04741 |