Hierarchical Multi-Agent Reinforcement Learning for Multi-Group Tax Game

Taxation is a fundamental instrument of economic policy and has long been studied through economic models. However, most existing taxation models focus on interactions within a single economic group, typically with one government and multiple households. Such a setting overlooks interactions among independent economic groups, where governments may compete for economic resources through fiscal policies. To capture these interactions, we formulate taxation as a hierarchical multi-group tax game. Within each group, a government sets tax policies while households respond, forming a hierarchical government–household interaction. Across groups, governments compete through fiscal policies, coupling the economic dynamics of distinct groups. This coupled structure poses challenges for standard multi-agent reinforcement learning (MARL) methods. To this end, we propose Multi-Group PPO (MGPPO), a bilevel MARL algorithm designed for hierarchical multi-group interactions. MGPPO incorporates Hierarchical Sampling to coordinate learning across agent levels and Curriculum Learning to improve training stability. We further develop a multi-group taxation simulation environment grounded in classical economic models, supporting the evaluation of fiscal policies under inter-group competition. Simulations show that in the two-group settings, MGPPO reduces the income and wealth Gini coefficients by 5.0% and 4.9%, respectively, and increases mean GDP by 4.1% compared with IPPO.