Work

Multi-agent reinforcement learning and variational inequality models for international trade networks under crisis

Year: 2025

Type: article

Abstract: Global disasters increasingly disrupt agricultural commodity flows, with food insecurity as a major consequence. Quantitative tools to assess such impacts are essential for resilience. We propose a hybrid Multi-Agent Reinforcement Learning (MARL) architecture to solve Variational Inequality (VI) problems in multi-commodity trade equilibria. While variational inequalities offer a rigorous method, their resolution via MARL faces stability and convergence challenges. Our actor–critic approach integrates a Gradient-based Learning Rate (GLR) scheduler, adaptive epsilon decay, prioritized replay, and a dual reward combining individual and centralized feedback. Agents representing supply and demand learn optimal strategies to reach equilibrium in simulated markets. Experiments, spanning stable conditions, dynamic price shifts, and route blockages, show faster convergence and stronger robustness than Multi-Agent Proximal Policy Optimization (MAPPO) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Results highlight the promise of MARL for simulating economic behavior and optimizing decentralized decision-making in complex systems.

Source: Simulation Modelling Practice and Theory

DOI: 10.1016/j.simpat.2025.103219

Authors: Laura De Natale, Georgia Fargetta, Laura R.M. Scrimali, Sebastiano Battiato

Institution: University of Catania

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,890