Sparse Curriculum Reinforcement Learning for End-to-End Driving

Abstract-- 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. Figure 1: End to End Driving. Performance of Deep RL in competing with humans in games like Atari [32], AlphaGo [46] and Dota [2] has shown its potential to solve complex decision making problems for navigation decision at intersections (e.g.