  It is still an open and challenging problem for mobile robots navigating
along time-efficient and collision-free paths in a crowd. The main challenge
comes from the complex and sophisticated interaction mechanism, which requires
the robot to understand the crowd and perform proactive and foresighted
behaviors. Deep reinforcement learning is a promising solution to this problem.
However, most previous learning methods incur a tremendous computational
burden. To address these problems, we propose a graph-based deep reinforcement
learning method, SG-DQN, that (i) introduces a social attention mechanism to
extract an efficient graph representation for the crowd-robot state; (ii)
directly evaluates the coarse q-values of the raw state with a learned dueling
deep Q network(DQN); and then (iii) refines the coarse q-values via online
planning on possible future trajectories. The experimental results indicate
that our model can help the robot better understand the crowd and achieve a
high success rate of more than 0.99 in the crowd navigation task. Compared
against previous state-of-the-art algorithms, our algorithm achieves an
equivalent, if not better, performance while requiring less than half of the
computational cost.
