# Exploring applications of deep reinforcement learning for real-world autonomous driving systems

arXiv: https://arxiv.org/abs/1901.01536 · HTML: https://mlsanity.com/paper/1901.01536/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2019.

## Built on by (most cited first, 18 of 18)

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- [Review of Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2302.06370/) (2023)
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## Abstract (arXiv)

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.