Machine Learning Combinatorial Frameworks for Architecture

Abstract

This paper investigates the application of machine learning for the simulation of larger architectural aggregations formed through the recombination of discrete components. This is primarily explored through establishing hardcoded assembly and connection logics which are used to form the framework of architectural fitness conditions for machine learning models. The key machine learning models researched are a combination of the deep reinforcement learning algorithm proximal policy optimization (PPO) and Generative Adversarial Imitation Learning (GAIL) in the Unity Machine Learning Agent asset toolkit. The goal of applying these machine learning models is to train the agent behaviours (discrete components) to learn specific logics of connection. In order to achieve assembled architectural `states that allow for spatial habitation through the process of simulation.