  In this paper we introduce the first reinforcement learning (RL) based
robotic navigation method which utilizes ultrasound (US) images as an input.
Our approach combines state-of-the-art RL techniques, specifically deep
Q-networks (DQN) with memory buffers and a binary classifier for deciding when
to terminate the task.
  Our method is trained and evaluated on an in-house collected data-set of 34
volunteers and when compared to pure RL and supervised learning (SL)
techniques, it performs substantially better, which highlights the suitability
of RL navigation for US-guided procedures. When testing our proposed model, we
obtained a 82.91% chance of navigating correctly to the sacrum from 165
different starting positions on 5 different unseen simulated environments.
