# Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car

arXiv: https://arxiv.org/abs/2308.13088 · HTML: https://mlsanity.com/paper/2308.13088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Average-reward model-free reinforcement learning: a systematic review and literature mapping](https://mlsanity.com/paper/2010.08920/) (2020)
- [Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey](https://mlsanity.com/paper/1904.04973/) (2019)
- [Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data](https://mlsanity.com/paper/2104.11106/) (2021)
- [Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations](https://mlsanity.com/paper/1905.05940/) (2019)
- [Autonomous Driving System Design for Formula Student Driverless Racecar](https://mlsanity.com/paper/1809.07636/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle](https://mlsanity.com/paper/2401.02903/) (2024)

## Abstract (arXiv)

With the rising popularity of autonomous navigation research, Formula Student (FS) events are introducing a Driverless Vehicle (DV) category to their event list. This paper presents the initial investigation into utilising Deep Reinforcement Learning (RL) for end-to-end control of an autonomous FS race car for these competitions. We train two state-of-the-art RL algorithms in simulation on tracks analogous to the full-scale design on a Turtlebot2 platform. The results demonstrate that our approach can successfully learn to race in simulation and then transfer to a real-world racetrack on the physical platform. Finally, we provide insights into the limitations of the presented approach and guidance into the future directions for applying RL toward full-scale autonomous FS racing.