  Surgical robotics is a rapidly evolving field that is transforming the
landscape of surgeries. Surgical robots have been shown to enhance precision,
minimize invasiveness, and alleviate surgeon fatigue. One promising area of
research in surgical robotics is the use of reinforcement learning to enhance
the automation level. Reinforcement learning is a type of machine learning that
involves training an agent to make decisions based on rewards and punishments.
This literature review aims to comprehensively analyze existing research on
reinforcement learning in surgical robotics. The review identified various
applications of reinforcement learning in surgical robotics, including
pre-operative, intra-body, and percutaneous procedures, listed the typical
studies, and compared their methodologies and results. The findings show that
reinforcement learning has great potential to improve the autonomy of surgical
robots. Reinforcement learning can teach robots to perform complex surgical
tasks, such as suturing and tissue manipulation. It can also improve the
accuracy and precision of surgical robots, making them more effective at
performing surgeries.
