Abstract: With the rapid development of emerging network technologies such as B5G/6G and Internet of Things, network services present characteristics of diverse business types, differentiated quality requirements, and dynamic resource demands. Under the Software Defined Networking (SDN) and Network Function Virtualization (NFV) paradigm, Service Function Chaining (SFC) technology enables customized network service deployment through flexible orchestration of Virtual Network Functions (VNF). However, SFC deployment in dynamic network environments faces challenges such as large decision space and complex, changing environments. Reinforcement learning (RL) demonstrates significant advantages in solving Service Function Chain deployment problems due to its capability to adaptively learn complex environmental characteristics and make dynamic decisions. Although research on applying RL to SFC deployment has made certain progress, a systematic and comprehensive review analysis remains lacking. To address this gap, the fundamental concepts and technical architecture of SFC deployment are elaborated, along with a specific introduction to the RL-based SFC deployment framework. Subsequently, from the perspectives of algorithm design, application scenarios, and optimization strategies, the research progress and innovative applications of reinforcement learning in three key phases—SFC placement, scheduling, and reconfiguration—are systematically examined and thoroughly analyzed. Finally, the advantages and limitations of existing research in terms of algorithm design, performance optimization, and practical deployment are summarized, while technical challenges and future development trends in this field are analyzed.