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arXiv · 2103.09189

Goal-constrained Sparse Reinforcement Learning for End-to-End Driving

Pranav Agarwal·Pierre de Beaucorps·Raoul de Charette
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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 full-control driving with only goal-constrained sparse reward 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 selected at inference by a navigation system. We demonstrate the ability of our proposal to generalize on unseen road layout, and to drive significantly longer than in the training.

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Pranav Agarwal, Pierre de Beaucorps, Raoul de Charette. 2021-03-16. Goal-constrained Sparse Reinforcement Learning for End-to-End Driving. https://arxiv.org/abs/2103.09189

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