Reinforcement-learning agents for architects' trade-offs in designing children's play environment: A qualitative comparative analysis

Abstract

Although contemporary children's learning environments highlight promoting physical and social development–related play behaviours and safety, there are no valid means to analyse children's dynamic, complex behaviours. To address this limitation, the paper explores the impacts of agent-based simulation on architects' trade-offs in designing children's play-oriented learning environments. To simulate children's subtle behavioural responsiveness to the given environments, this paper adopts reinforcement learning (RL) as a method to develop autonomous play behaviours. A comparative experiment was conducted with 14 professional architects to investigate the capacities of the RL-powered agents. The systemic qualitative analysis indicates that the RL agent supported the coordination of complex physical constraints and new insights into child-oriented dimensions when evaluating the learning environment design.