  Mobile robot navigation in complex and dynamic environments is a challenging
but important problem. Reinforcement learning approaches fail to solve these
tasks efficiently due to reward sparsities, temporal complexities and
high-dimensionality of sensorimotor spaces which are inherent in such problems.
We present a novel approach to train action policies to acquire navigation
skills for wheel-legged robots using deep reinforcement learning. The policy
maps height-map image observations to motor commands to navigate to a target
position while avoiding obstacles. We propose to acquire the multifaceted
navigation skill by learning and exploiting a number of manageable navigation
behaviors. We also introduce a domain randomization technique to improve the
versatility of the training samples. We demonstrate experimentally a
significant improvement in terms of data-efficiency, success rate, robustness
against irrelevant sensory data, and also the quality of the maneuver skills.
