# RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning

arXiv: https://arxiv.org/abs/2403.07129 · HTML: https://mlsanity.com/paper/2403.07129/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [Action Mapping for Reinforcement Learning in Continuous Environments with Constraints](https://mlsanity.com/paper/2412.04327/) (2024)
- [RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms](https://mlsanity.com/paper/2501.17311/) (2025)
- [Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization](https://mlsanity.com/paper/2603.12960/) (2026)
- [Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing](https://mlsanity.com/paper/2604.09499/) (2026)
- [Context-Aware Model-Based Reinforcement Learning for Autonomous Racing](https://mlsanity.com/paper/2510.11501/) (2025)
- [Action Shaping: Policies Absorb What They Can Express](https://mlsanity.com/paper/2609.32752/) (2026)

## Abstract (arXiv)

The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. To address this, we introduce RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that rely on predefined racing lines, RaceMOP operates without a map, utilizing only local observations to execute high-speed overtaking maneuvers. Our approach combines an artificial potential field method as a base policy with residual policy learning to enable long-horizon planning. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Extensive experiments on twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during overtaking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners and generalizes to unknown racetracks, affirming its potential for broader applications in robotics. Our code is available at http://github.com/raphajaner/racemop.