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Energy optimization leveraging artificially intelligent algorithms has beenproven effective. However, when buildings are commissioned, there is nohistorical data that could be used to train these algorithms. On-lineReinforcement Learning (RL) algorithms have shown significant promise, buttheir deployment carries a significant risk, because as the RL agent initiallyexplores its action space it could cause significant discomfort to the buildingresidents. In this paper we present ReLBOT - a new technique that uses transferlearning 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-upperiod. We demonstrate improvements of up to 6.2 times in the duration, and upto 132 times in prediction variance, for the reinforcement learning agent'swarm-up period.

A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings