# A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing

arXiv: https://arxiv.org/abs/2209.15073 · HTML: https://mlsanity.com/paper/2209.15073/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning](https://mlsanity.com/paper/2109.08273/) (2021)
- [CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving](https://mlsanity.com/paper/1807.03776/) (2018)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning](https://mlsanity.com/paper/2107.08325/) (2021)
- [Learning from Simulation, Racing in Reality](https://mlsanity.com/paper/2011.13332/) (2020)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom](https://mlsanity.com/paper/2209.11181/) (2022)

## Built on by (most cited first, 12 of 12)

- [MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts](https://mlsanity.com/paper/2303.00638/) (2023)
- [Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction](https://mlsanity.com/paper/2304.00194/) (2023)
- [Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning](https://mlsanity.com/paper/2407.18962/) (2024)
- [Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training](https://mlsanity.com/paper/2502.03822/) (2025)
- [TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing](https://mlsanity.com/paper/2410.07447/) (2024)
- [PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking](https://mlsanity.com/paper/2504.20359/) (2025)
- [Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging](https://mlsanity.com/paper/2409.05289/) (2024)
- [Optimizing Active Perception for Learning Simultaneous Viewpoint Selection and Manipulation with Diffusion Policy](https://mlsanity.com/paper/2409.14615/) (2024)
- [Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2308.13491/) (2023)
- [Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generation](https://mlsanity.com/paper/2511.16807/) (2025)
- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)
- [Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control](https://mlsanity.com/paper/2604.03023/) (2026)

## Abstract (arXiv)

Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driving at the limits of the vehicle's handling. To train agile control policies for autonomous racing, learning-based approaches largely utilize reinforcement learning, albeit with mixed results. In this study, we benchmark a variety of imitation learning policies for racing vehicles that are applied directly or for bootstrapping reinforcement learning both in simulation and on scaled real-world environments. We show that interactive imitation learning techniques outperform traditional imitation learning methods and can greatly improve the performance of reinforcement learning policies by bootstrapping thanks to its better sample efficiency. Our benchmarks provide a foundation for future research on autonomous racing using Imitation Learning and Reinforcement Learning.