Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach
Shashi Suman, Ali Etemad, Francois Rivest

TL;DR
This paper explores how smart homes can influence human behavior by using reinforcement learning models to simulate human activities and preferences, revealing potential behavioral adaptations and unexpected activity switches.
Contribution
It introduces a hierarchical reinforcement learning human model integrated with a smart home simulation, demonstrating adaptive thermal preferences and behavioral changes.
Findings
Smart homes can anticipate and adapt to human thermal preferences.
Behavioral variations can lead to activity switching due to smart home influence.
Reduced time to adjust thermal settings indicates improved comfort adaptation.
Abstract
We aim to investigate the potential impacts of smart homes on human behavior. To this end, we simulate a series of human models capable of performing various activities inside a reinforcement learning-based smart home. We then investigate the possibility of human behavior being altered as a result of the smart home and the human model adapting to one-another. We design a semi-Markov decision process human task interleaving model based on hierarchical reinforcement learning that learns to make decisions to either pursue or leave an activity. We then integrate our human model in the smart home which is based on Q-learning. We show that a smart home trained on a generic human model is able to anticipate and learn the thermal preferences of human models with intrinsic rewards similar to the generic model. The hierarchical human model learns to complete each activity and set optimal thermal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
