  Robotic catheterization is typically used for percutaneous coronary
intervention procedures nowadays and it involves steering flexible endovascular
tools to open up occlusion in the coronaries. In this study, a sample-efficient
deep reinforcement learning with episodic policy transfer is, for the first
time, used for motion control during robotic catheterization with fully
adaptive PID tuning strategy. The reinforcement model aids the agent to
continuously learn from its interactions in its environment and adaptively tune
PID control gains for axial navigation of endovascular tool. The model was
validated for axial motion control of a robotic system designed for
intravascular catheterization. Simulation and experimental trials were done to
validate the application of the model, and results obtained shows it could
self-tune PID gains appropriately for motion control of a robotic catheter
system. Performance comparison with conventional methods in average of 10
trials shows the agent tunes the gain better with error of 0.003 mm. Thus, the
proposed model would offer more stable set-point motion control robotic
catheterization.
