  Despite significant advancements in the field of multi-agent navigation,
agents still lack the sophistication and intelligence that humans exhibit in
multi-agent settings. In this paper, we propose a framework for learning a
human-like general collision avoidance policy for agent-agent interactions in
fully decentralized, multi-agent environments. Our approach uses knowledge
distillation with reinforcement learning to shape the reward function based on
expert policies extracted from human trajectory demonstrations through behavior
cloning. We show that agents trained with our approach can take human-like
trajectories in collision avoidance and goal-directed steering tasks not
provided by the demonstrations, outperforming the experts as well as
learning-based agents trained without knowledge distillation.
