  Training an agent to navigate with DRL is data-hungry, which requires
millions of training steps. Besides, the DRL agents performing well in training
scenarios are found to perform poorly in some unseen real-world scenarios. In
this paper, we discuss why the DRL agent fails in such unseen scenarios and
find the representation of LiDAR readings is the key factor behind the agent's
performance degradation. Moreover, we propose an easy, but efficient input
pre-processing (IP) approach to accelerate training and enhance the performance
of the DRL agent in such scenarios. The proposed IP functions can highlight the
important short-distance values of laser scans and compress the range of
less-important long-distance values. Extensive comparative experiments are
carried out, and the experimental results demonstrate the high performance of
the proposed IP approaches.
