AbstractThe construction engineering and management (CEM) domain frequently meets complex tasks due to the unavoidable complicated operation environments and the involvement of numerous workers. Being able to simulate these tasks with promised designed goals, reinforcement learning (RL) can help CEM engineers reach enhanced strategies in multi-/single-objective sequential decision-making under various sources of uncertainties. To provide a better understanding of the status quo of the RL application in CEM and its potential benefits with their strengths and limitations, this study systematically reviewed 85 CEM-related RL-based studies as a result of queries from three main scientific databases, namely Scopus, Science Direct, and Web of Science. The results of this review reveal that researchers have been increasingly applying RL methods in CEM domains, such as building energy management, infrastructure management, construction machinery, and even safety in the last few years. Our analysis showed that the reviewed papers are associated with different limitations such as generalizability, justification of selecting the approaches, and validation. This review paper alongside the presented overview of the RL methodology can assist researchers and practitioners in CEM with (1) gaining a high-level and intuitive understanding of RL algorithms, (2) identifying previous and possible future opportunities for applying RL in complex decision-making, and (3) fine tuning, proper validation, and optimizing to-be-developed RL frameworks in their future studies and applications.