# Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing

arXiv: https://arxiv.org/abs/2312.06406 · HTML: https://mlsanity.com/paper/2312.06406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 46 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.07971/) (2020)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Regularizing Action Policies for Smooth Control with Reinforcement Learning](https://mlsanity.com/paper/2012.06644/) (2020)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [Learning to Perform Physics Experiments via Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01843/) (2016)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller](https://mlsanity.com/paper/1910.11309/) (2019)
- [Learning from Simulation, Racing in Reality](https://mlsanity.com/paper/2011.13332/) (2020)
- [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020)
- [Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing](https://mlsanity.com/paper/2209.04346/) (2022)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09370/) (2022)
- [The Autonomous Racing Software Stack of the KIT19d](https://mlsanity.com/paper/2010.02828/) (2020)
- [Design of an Autonomous Racecar: Perception, State Estimation and System Integration](https://mlsanity.com/paper/1804.03252/) (2018)
- [Learning the Subsystem of Local Planning for Autonomous Racing](https://mlsanity.com/paper/2102.11042/) (2021)
- [Reward Signal Design for Autonomous Racing](https://mlsanity.com/paper/2103.10098/) (2021)
- [The Software Stack That Won the Formula Student Driverless Competition](https://mlsanity.com/paper/2210.10933/) (2022)
- [Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning](https://mlsanity.com/paper/2205.09658/) (2022)

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

- [Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/2401.11792/) (2024)

## Abstract (arXiv)

In this paper, we address the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars when navigating under conditions where practical vehicle modelling errors (commonly known as model mismatches) are present. To address this challenge, we propose a partial end-to-end algorithm that decouples the planning and control tasks. Within this framework, an RL agent generates a trajectory comprising a path and velocity, which is subsequently tracked using a pure pursuit steering controller and a proportional velocity controller, respectively. In contrast, many current learning-based (i.e., reinforcement and imitation learning) algorithms utilise an end-to-end approach whereby a deep neural network directly maps from sensor data to control commands. By leveraging the robustness of a classical controller, our partial end-to-end driving algorithm exhibits better robustness towards model mismatches than standard end-to-end algorithms.