  In this paper, we present an algorithm to efficiently learn
socially-compliant navigation policies from observations of human trajectories.
As mobile robots come to inhabit and traffic social spaces, they must account
for social cues and behave in a socially compliant manner. We focus on learning
such cues from examples. We describe an inverse reinforcement learning based
algorithm which learns from human trajectory observations without knowing their
specific actions. We increase the sample-efficiency of our approach over
alternative methods by leveraging the notion of a replay buffer (found in many
off-policy reinforcement learning methods) to eliminate the additional sample
complexity associated with inverse reinforcement learning. We evaluate our
method by training agents using publicly available pedestrian motion data sets
and compare it to related methods. We show that our approach yields better
performance while also decreasing training time and sample complexity.
