  Deep reinforcement Learning for end-to-end driving is limited by the need of
complex reward engineering. Sparse rewards can circumvent this challenge but
suffers from long training time and leads to sub-optimal policy. In this work,
we explore driving using only goal conditioned sparse rewards and propose a
curriculum learning approach for end to end driving using only navigation view
maps that benefit from small virtual-to-real domain gap. To address the
complexity of multiple driving policies, we learn concurrent individual
policies which are selected at inference by a navigation system. We demonstrate
the ability of our proposal to generalize on unseen road layout, and to drive
longer than in the training.
