Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing
METADATA ONLY
Loading...
Author / Producer
Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Autonomous racing presents unique challenges due to its non-linear dynamics, the high speed involved, and the critical need for real-time decision-making under dynamic and unpredictable conditions. Most traditional Reinforcement Learning (RL) approaches rely on extensive simulation-based pre-training, which faces crucial challenges in transfer effectively to real-world environments. This paper introduces a robust on-board RL framework for autonomous racing, designed to eliminate the dependency on simulation-based pre-training enabling direct real-world adaptation. The proposed system introduces a refined Soft Actor-Critic (SAC) algorithm, leveraging a residual RL structure to enhance classical controllers in real-time by integrating multi-step Temporal-Difference (TD) learning, an asynchronous training pipeline, and Heuristic Delayed Reward Adjustment (HDRA) to improve sample efficiency and training stability. The framework is validated through extensive experiments on the F1TENTH racing platform, where the residual RL controller consistently outperforms the baseline controllers and achieves up to an 11.5 % reduction in lap times compared to the State-of-the-Art (SotA) with only 20 min of training. Additionally, an End-to-End (E2E) RL controller trained without a baseline controller surpasses the previous best results with sustained on-track learning. These findings position the framework as a robust solution for high-performance autonomous racing and a promising direction for other real-time, dynamic autonomous systems.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
6
Pages / Article No.
829 - 861
Publisher
Manning College of Information and Computer Sciences, University of Massachusetts Amherst
Event
2nd Reinforcement Learning Conference (RLC 2025)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Autonomous Racing; Physical Robot Learning
Organisational unit
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning
Notes
Funding
Related publications and datasets
Is new version of: https://doi.org/10.48550/arXiv.2505.07321