Differentiable Learning of Scalable Multi-Agent Navigation Policies

We present an end-to-end differentiable learning algorithm for multi-agent navigation policies. Compared with prior model-free learning algorithms, our method leads to a significant speedup via the gradient information. Our key innovation lies in a novel differentiability analysis of the optimization-based crowd simulation algorithm via the implicit function theorem. Inspired by continuum multi-agent modeling techniques, we further propose a kernel-based policy parameterization, allowing our learned policy to scale up to an arbitrary number of agents without re-training. We evaluate our algorithm on two tasks in obstacle-rich environments, partially labeled navigation and evacuation, for which loss functions can be defined making the entire task learnable in an end-to-end manner. The results show that our method can achieve more than one order of magnitude speedup over model-free baselines and readily scale to unseen target configurations and agent sizes.