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Modern control solutions for HVAC have demonstrated excellent cost and energy savings through the utilisation of machine learning techniques. However, a challenging problem faced by most machine learning tasks is the amount of time and data required to train effective policies in the absence of prior knowledge. Considering that buildings from a specific geographical location share common environmental and structural features, this paper investigates the impact of spatial changes on performance accuracy through the use of transfer learning applied to reinforcement learning based HVAC control. We propose the development of an adapted RL (Q-learning) algorithm which can transfer HVAC control polices, adjusting themselves according to spatial changes. We examine the performance of our approach across multiple different locations. Moreover, an analysis of the user’s time out comfort has been made …