A multi-objective reinforcement learning approach for furniture arrangement with optimal IEQ in multi-occupant offices

Abstract

Indoor Environmental Quality (IEQ) concerns several aspects of environmental comforts, such as thermal, visual and acoustics comfort. In particular, IEQ plays a relevant role in workers’ satisfaction since it strongly influences health, well-being, and productivity. Specifically, it has been demonstrated that the furniture configuration in working spaces affects the occupant’s comfort perception. Nevertheless, IEQ has been either neglected or partially addressed in the context of interior design. The contribution of this paper is to introduce a novel method for furniture layout optimisation in terms of IEQ requirements in multi-occupant offices. In particular, we explore the furniture arrangement task as a Multi-Objective Markov Decision Process (MOMDP), which is solved by a reinforcement learning (RL) agent. The goal is to determine optimal workstation positions that maximise workers’ IEQ satisfaction and functional aspects of working spaces under analysis. Firstly, we formulated the furniture layout task as a MOMDP problem by defining reward functions in terms of thermal, acoustics and visual comfort. Then, we train the RL agent to produce optimal/suboptimal layout patterns through a Q-learning-based algorithm. We conducted experiments in two different offices. The experimental results demonstrated that the proposed multi-objective RL approach is able to determine optimal furniture arrangements that provide a balance among office occupants in terms of IEQ satisfaction. Moreover, numerical results show that the proposed approach can be a valuable tool for evaluating the conformity to the environmental comfort standard of working environments during the furniture layout design phase instead of applying corrections during the post-occupancy evaluation.