Simulation Modelling Practice and Theory
I’m very pleased to share that our latest paper, “Multi-agent reinforcement learning and variational inequality models for international trade networks under crisis”, has just been published in Simulation Modelling Practice and Theory (Elsevier, Q1 in Computer Science). https://doi.org/10.1016/j.simpat.2025.103219
This work, co-authored with Laura De Natale, Laura R.M. Scrimali, and [Sebastiano Battiato](https://www.linkedin.com/home?originalSubdomain=it#), introduces a hybrid Multi-Agent Reinforcement Learning (MARL) framework for solving variational inequality models in multi-commodity trade networks.
Our method bridges the mathematical rigor of equilibrium theory with the adaptive learning capabilities of MARL, addressing complex global trade dynamics, especially under crisis and disruption scenarios affecting food security and supply chains.
We demonstrate how the integration of Gradient-based Learning Rate scheduling (GLR), adaptive exploration, and dual reward mechanisms enables faster convergence and improved stability compared to MAPPO and MADDPG baselines.
Grateful to all co-authors and collaborators for their valuable contributions and discussions that made this work possible.
https://www.sciencedirect.com/science/article/pii/S1569190X25001546?via%3Dihub