Lukasz Pelcner

Multi Objective Quantile Based Reinforcement Learning for Modern Urban Planning

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