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Medical image registration is a pivotal application within the field of medical imaging. It entails the fusion of commonalities among data of disparate modalities into a unified coordinate system, thereby achieving complementary imaging information. This process plays a crucial role in scenarios such as accurate disease diagnosis, surgical guidance, patient monitoring, and radiological therapy. Due to the distinct characteristics exhibited by changes in the same region over time or across different modalities, swiftly and accurately identifying corresponding relationships between pre-registered images remains a formidable challenge. This paper proposes the incorporation of an enhanced Artificial Rabbits Optimization (ARO) algorithm as an agent within the framework of reinforcement learning. The method, referred to as RL-mARO, is employed for medical image registration. By utilizing Normalized Mutual Information …