# Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.07971 · HTML: https://mlsanity.com/paper/2008.07971/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 30 of 65)

- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles](https://mlsanity.com/paper/2111.12083/) (2021)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals](https://mlsanity.com/paper/2302.04449/) (2023)
- [CaRL: Learning Scalable Planning Policies with Simple Rewards](https://mlsanity.com/paper/2504.17838/) (2025)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Emergence of In-Context Reinforcement Learning from Noise Distillation](https://mlsanity.com/paper/2312.12275/) (2023)
- [Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing](https://mlsanity.com/paper/2103.04909/) (2021)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [On Expressivity and Trainability of Quadratic Networks](https://mlsanity.com/paper/2110.06081/) (2021)
- [Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning](https://mlsanity.com/paper/2107.03961/) (2021)
- [FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing](https://mlsanity.com/paper/2304.09831/) (2023)
- [Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2311.10747/) (2023)
- [Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment](https://mlsanity.com/paper/2101.08477/) (2021)
- [Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight](https://mlsanity.com/paper/2309.09865/) (2023)
- [A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo](https://mlsanity.com/paper/2406.12563/) (2024)
- [Safe Autonomous Racing via Approximate Reachability on Ego-vision](https://mlsanity.com/paper/2110.07699/) (2021)
- [Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models](https://mlsanity.com/paper/2504.11514/) (2025)
- [A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games](https://mlsanity.com/paper/2509.03682/) (2025)
- [TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09370/) (2022)
- [Learning Robot Manipulation from Cross-Morphology Demonstration](https://mlsanity.com/paper/2304.03833/) (2023)
- [Residual Policy Learning for Vehicle Control of Autonomous Racing Cars](https://mlsanity.com/paper/2302.07035/) (2023)
- [Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2306.08388/) (2023)
- [BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay](https://mlsanity.com/paper/2402.14194/) (2024)
- [Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing](https://mlsanity.com/paper/2205.02953/) (2022)
- [Event Tables for Efficient Experience Replay](https://mlsanity.com/paper/2211.00576/) (2022)
- [Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control](https://mlsanity.com/paper/2311.18393/) (2023)
- [Enhancing Reinforcement Learning Agents with Local Guides](https://mlsanity.com/paper/2402.13930/) (2024)

## Abstract (arXiv)

Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem. In the present work, we propose a learning-based system for autonomous car racing by leveraging a high-fidelity physical car simulation, a course-progress proxy reward, and deep reinforcement learning. We deploy our system in Gran Turismo Sport, a world-leading car simulator known for its realistic physics simulation of different race cars and tracks, which is even used to recruit human race car drivers. Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of over 50,000 human players.