Most unmanned clusters are typical mobile self-organizing networks (MANETs) with high dynamics and energy constraints. Deep reinforcement learning (DRL) is an emerging hotspot in the field of machine learning because it can make decisions without relying on models and is very suitable for dynamic unmanned cluster communication systems with time-varying network conditions. DRL -based unmanned cluster routing protocols are of great research value and significance. This paper introduces the fundamental characteristics of unmanned clusters and dynamic routing protocols; briefly describes the fundamental elements of DRL algorithms and their applicability in dynamic routing protocols; reviews the applications of DRL algorithms in unmanned clusters routing protocols; and summarizes the challenges and future development directions of unmanned clusters routing protocols based on DRL.