  In this paper, we present a novel approach to navigating endoluminal
channels, specifically within the bronchial tubes, using Q-learning, a
reinforcement learning algorithm. The proposed method involves training a
Q-learning agent to navigate a simulated environment resembling bronchial
tubes, with the ultimate goal of enabling the navigation of real bronchial
tubes. We discuss the formulation of the problem, the simulation environment,
the Q-learning algorithm, and the results of our experiments. Our results
demonstrate the agent's ability to learn effective navigation strategies and
reach predetermined goals within the simulated environment. This research
contributes to the development of autonomous robotic systems for medical
applications, particularly in challenging anatomical environments.
