  This paper studies how to improve the generalization performance and learning
speed of the navigation agents trained with deep reinforcement learning (DRL).
Although DRL exhibits huge potential in robot mapless navigation, DRL agents
performing well in training scenarios are often found to perform poorly in
unfamiliar scenarios. In this work, we propose that the representation of LiDAR
readings is a key factor behind the degradation of agents' performance and
present a powerful input pre-processing (IP) approach to address this issue. As
this approach uses adaptively parametric reciprocal functions to pre-process
LiDAR readings, we refer to this approach as IPAPRec and its normalized version
as IPAPRecN. IPAPRec/IPAPRecN can highlight important short-distance values and
compress the range of less-important long-distance values in laser scans, which
well address the issues induced by conventional representations of laser scans.
Their high performance was validated by extensive simulation and real-world
experiments. The results show that our methods can substantially improve
navigation agents' generalization performance and greatly reduce the training
time compared to conventional methods.
