# End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing

arXiv: https://arxiv.org/abs/2309.00296 · HTML: https://mlsanity.com/paper/2309.00296/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks](https://mlsanity.com/paper/2001.05992/) (2020)
- [Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies](https://mlsanity.com/paper/2006.06091/) (2020)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [A Comparison of Modern General-Purpose Visual SLAM Approaches](https://mlsanity.com/paper/2107.07589/) (2021)

## Built on by (most cited first, 3 of 3)

- [Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning](https://mlsanity.com/paper/2505.08264/) (2025)
- [Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving](https://mlsanity.com/paper/2505.06737/) (2025)
- [Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform](https://mlsanity.com/paper/2607.24320/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL) has emerged as a transformative approach in the domains of automation and robotics, offering powerful solutions to complex problems that conventional methods struggle to address. In scenarios where the problem definitions are elusive and challenging to quantify, learning-based solutions such as RL become particularly valuable. One instance of such complexity can be found in the realm of car racing, a dynamic and unpredictable environment that demands sophisticated decision-making algorithms. This study focuses on developing and training an RL agent to navigate a racing environment solely using feedforward raw lidar and velocity data in a simulated context. The agent's performance, trained in the simulation environment, is then experimentally evaluated in a real-world racing scenario. This exploration underlines the feasibility and potential benefits of RL algorithm enhancing autonomous racing performance, especially in the environments where prior map information is not available.