2025
Multi Objective Quantile Based Reinforcement Learning for Modern Urban Planning
Lukasz Pelcner, Leandro Soriano Marcolino, Matheus Aparecido do Carmo Alves, Paula A. Harrison, Peter M. Atkinson
IJCAI 2025
We present a novel Multi-Agent Reinforcement Learning approach to understand and improve policy development by land-shaping agents, such as governments and institutional bodies. We derive the underlying policy decisions by analyzing the land and developing an intelligent system that proposes optimal