Economics-Informed and Coordination-Aware Multi-Agent RL for Multi-Government Economic Policy Design

Reinforcement learning (RL) for economic policy design faces two persistent failures beyond toy settings: learned policies can drift away from basic economic sense, and independently trained fiscal, monetary, and pension authorities can conflict after deployment. We address both with two plug-in mechanisms for economic Markov games. Economics-Informed MARL (EI-MARL) regularises each policy toward classical economic rules through an annealed KL prior and Euler consistency. Multi-Government Coordination via Latent Communication (MG-Coord) lets heterogeneous government agents exchange latent intent messages and aligns their gradients with a conflict-aware regulariser while preserving separate institutional rewards. Across representative tasks, EI-MARL improves sample efficiency 3.4$\times$ and long-run welfare 18.1%; in the three-government benchmark, MG-Coord raises GDP by 27.1% and lowers the Gini coefficient by 8 points over communication-only coordination. Together, the mechanisms outperform the strongest previously reported hybrid baseline while adding <2% per-step overhead at $10^4$ agents.