  We present a novel sensor-based learning navigation algorithm to compute a
collision-free trajectory for a robot in dense and dynamic environments with
moving obstacles or targets. Our approach uses deep reinforcement
learning-based expert policy that is trained using a sim2real paradigm. In
order to increase the reliability and handle the failure cases of the expert
policy, we combine with a policy extraction technique to transform the
resulting policy into a decision tree format. The resulting decision tree has
properties which we use to analyze and modify the policy and improve
performance on navigation metrics including smoothness, frequency of
oscillation, frequency of immobilization, and obstruction of target. We are
able to modify the policy to address these imperfections without retraining,
combining the learning power of deep learning with the control of
domain-specific algorithms. We highlight the benefits of our algorithm in
simulated environments and navigating a Clearpath Jackal robot among moving
pedestrians.
