# A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7

arXiv: https://arxiv.org/abs/2504.09021 · HTML: https://mlsanity.com/paper/2504.09021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 28 of 28)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Reinforcement Learning with Augmented Data](https://mlsanity.com/paper/2004.14990/) (2020)
- [Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning](https://mlsanity.com/paper/2107.09645/) (2021)
- [Improving Sample Efficiency in Model-Free Reinforcement Learning from Images](https://mlsanity.com/paper/1910.01741/) (2019)
- [The Primacy Bias in Deep Reinforcement Learning](https://mlsanity.com/paper/2205.07802/) (2022)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [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)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning](https://mlsanity.com/paper/2306.10711/) (2023)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning](https://mlsanity.com/paper/2107.08325/) (2021)
- [A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo](https://mlsanity.com/paper/2406.12563/) (2024)
- [Controlling an Autonomous Vehicle with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.12153/) (2019)
- [Optimization-Based Hierarchical Motion Planning for Autonomous Racing](https://mlsanity.com/paper/2003.04882/) (2020)
- [AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility](https://mlsanity.com/paper/2409.15783/) (2024)
- [Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data](https://mlsanity.com/paper/2104.11106/) (2021)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data](https://mlsanity.com/paper/2407.16680/) (2024)
- [Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine](https://mlsanity.com/paper/2303.08235/) (2023)
- [Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing](https://mlsanity.com/paper/2309.10716/) (2023)
- [Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar](https://mlsanity.com/paper/2005.07127/) (2020)

## Built on by (most cited first, 4 of 4)

- [Agile Flight Emerges from Multi-Agent Competitive Racing](https://mlsanity.com/paper/2512.11781/) (2025)
- [Automated Reward Design for Gran Turismo](https://mlsanity.com/paper/2511.02094/) (2025)
- [Multi-Timescale Hierarchical Reinforcement Learning for Unified Behavior and Control of Autonomous Driving](https://mlsanity.com/paper/2506.23771/) (2025)
- [Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation](https://mlsanity.com/paper/2606.09236/) (2026)

## Abstract (arXiv)

Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global features that require instrumentation external to a car, such as precise localization of agents and opponents, limiting real-world applicability. To address this limitation, we introduce a vision-based autonomous racing agent that relies solely on ego-centric camera views and onboard sensor data, eliminating the need for precise localization during inference. This agent employs an asymmetric actor-critic framework: the actor uses a recurrent neural network with the sensor data local to the car to retain track layouts and opponent positions, while the critic accesses the global features during training. Evaluated in GT7, our agent consistently outperforms GT7's built-drivers. To our knowledge, this work presents the first vision-based autonomous racing agent to demonstrate champion-level performance in competitive racing scenarios.