Sparse Curriculum Reinforcement Learning for End-to-End Driving

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.