Vision based driving agent for race car simulation environments
On the map: End-to-end autonomous driving → Reinforcement learning for driving
Abstract (arXiv)
In recent years, autonomous driving has become a popular field of study. As control at tire grip limit is essential during emergency situations, algorithms developed for racecars are useful for road cars too. This paper examines the use of Deep Reinforcement Learning (DRL) to solve the problem of grip limit driving in a simulated environment. Proximal Policy Optimization (PPO) method is used to train an agent to control the steering wheel and pedals of the vehicle, using only visual inputs to achieve professional human lap times. The paper outlines the formulation of the task of time optimal driving on a race track as a deep reinforcement learning problem, and explains the chosen observations, actions, and reward functions. The results demonstrate human-like learning and driving behavior that utilize maximum tire grip potential.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 7 of its 13 references that are in the map. First posted on arXiv 14 Apr 2025.
Builds on
7 of 13 references.
References · 7
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
- 2019Solving Rubik's Cube with a Robot Handcited by 734
- 2020Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learningcited by 65
- 2021Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Datacited by 10
- 2021Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learningcited by 3
Built on by
Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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- 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
- 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
- 2025A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7