  The building sector consumes the largest energy in the world, and there have
been considerable research interests in energy consumption and comfort
management of buildings. Inspired by recent advances in reinforcement learning
(RL), this paper aims at assessing the potential of RL in building climate
control problems with occupant interaction. We apply a recent RL approach,
called DDPG (deep deterministic policy gradient), for the continuous building
control tasks and assess its performance with simulation studies in terms of
its ability to handle (a) the partial state observability due to sensor
limitations; (b) complex stochastic system with high-dimensional state-spaces,
which are jointly continuous and discrete; (c) uncertainties due to ambient
weather conditions, occupant's behavior, and comfort feelings. Especially, the
partial observability and uncertainty due to the occupant interaction
significantly complicate the control problem. Through simulation studies, the
policy learned by DDPG demonstrates reasonable performance and computational
tractability.
