Spatial Agent-based Architecture Design Simulation Systems

Abstract

This paper presents case studies and analysis of agent-based reinforcement learning (RL) systems towards practical applications for specific architecture/engineering tasks using Unity 3D-based simulation methods. Finding and implementing sufficient abstraction for architecture and engineering problems to be solved by agent-based systems requires broad architectural knowledge and the ability to break down complex problems. Modern artificial intelligence (AI) and machine learning (ML) systems based on artificial neural networks can solve complex problems in different domains such as computer vision, language processing, and predictive maintenance. The paper will give a theoretical overview, such as more theoretical abstractions like zero-sum games, and a comparison of presented games. The application section describes a possible categorization of practical usages. From more general applications to more narrowed ones, we explore current possibilities of RL application in the field of relatable problems. We use the Unity 3D engine as the basis of a robust simulation environment.