Personal Comfort Estimation in Partial Observable Environment using Reinforcement Learning

The technology used in smart homes have improved to learn the user preferences from feedbacks in order to provide convenience to the user in the home environment. Most smart homes learn a uniform model to represent the thermal preference of user which generally fails when the pool of occupants includes people having different age, gender, and location. Having different thermal sensation for each user poses a challenge for the smart homes to learn a personalized preference for each occupant without forgetting the policy of others. A smart home with single optimal policy may fail to provide comfort when a new user with different preference is integrated in the home. In this paper, we propose POSHS, a Bayesian Reinforcement learning algorithm that can approximate the current occupant state in a partial observable environment using its thermal preference and then decide if its a new occupant or belongs to the pool of previously observed users. We then compare POSHS algorithm with an LSTM based algorithm to learn and estimate the current state of the occupant while also taking optimal actions to reduce the timesteps required to set the preferences. We perform these experiments with upto 5 simulated human models each based on hierarchical reinforcement learning. The results show that POSHS can approximate the current user state just from its temperature and humidity preference and also reduce the number of time-steps required to set optimal temperature and humidity by the human model in the presence of the smart home.