Smart buildings aim to optimize energy consumption by applying artificial intelligent algorithms. When a smart building is 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 smart building, to the newly commissioning 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.
翻译:智能建筑旨在通过应用人工智能算法来优化能源消耗。当智能建筑委派时,没有历史数据可用于训练这些算法。在线强化学习算法显示出极大的潜力,但其部署存在重大风险,因为当强化学习代理初步探索其行动空间时,它可能会对建筑居民造成重大不适。在本文中,我们提出了 ReLBOT,一种新技术,它结合深度强化学习使用迁移学习,从现有的优化智能建筑中转移知识到新建委派的建筑中,以减少强化学习代理预热期的不良影响。我们证明了在强化学习代理预热期中,持续时间和预测方差分别提高了6.2倍和132倍。