  This paper focuses on the acquisition of mapless navigation skills within
unknown environments. We introduce the Skill Q-Network (SQN), a novel
reinforcement learning method featuring an adaptive skill ensemble mechanism.
Unlike existing methods, our model concurrently learns a high-level skill
decision process alongside multiple low-level navigation skills, all without
the need for prior knowledge. Leveraging a tailored reward function for mapless
navigation, the SQN is capable of learning adaptive maneuvers that incorporate
both exploration and goal-directed skills, enabling effective navigation in new
environments. Our experiments demonstrate that our SQN can effectively navigate
complex environments, exhibiting a 40% higher performance compared to baseline
models. Without explicit guidance, SQN discovers how to combine low-level skill
policies, showcasing both goal-directed navigations to reach destinations and
exploration maneuvers to escape from local minimum regions in challenging
scenarios. Remarkably, our adaptive skill ensemble method enables zero-shot
transfer to out-of-distribution domains, characterized by unseen observations
from non-convex obstacles or uneven, subterranean-like environments.
