  Energy optimization leveraging artificially intelligent algorithms has been
proven effective. However, when buildings are commissioned, there is no
historical data that could be used to train these algorithms. On-line
Reinforcement Learning (RL) algorithms have shown significant promise, but
their deployment carries a significant risk, because as the RL agent initially
explores its action space it could cause significant discomfort to the building
residents. In this paper we present ReLBOT - a new technique that uses transfer
learning in conjunction with deep RL to transfer knowledge from an existing,
optimized and instrumented building, to the newly commissioning smart building,
to reduce the adverse impact of the reinforcement learning agent's warm-up
period. We demonstrate improvements of up to 6.2 times in the duration, and up
to 132 times in prediction variance, for the reinforcement learning agent's
warm-up period.
