Towards Personalization of User Preferences in Partially Observable Smart Home Environments

no code implementations • 2 Dec 2021 • Shashi Suman, Francois Rivest, Ali Etemad

In this paper, we propose a Bayesian Reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference, and then identify the occupant as a new user or someone is already known to the system.

Hierarchical Reinforcement Learning reinforcement-learning +1

Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach

no code implementations • 26 Feb 2021 • Shashi Suman, Ali Etemad, Francois Rivest

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.

Hierarchical Reinforcement Learning Q-Learning +2