Autonomous Driving with RL Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated April 2026

RL-based approaches to autonomous driving, from simulation to deployment.

1. Flow: A Modular Learning Framework for Mixed Autonomy Traffic
   Wu et al.. IEEE Trans. Robotics 2022.
2. Nocturne: A Scalable Driving Benchmark for Bringing Multi-Agent Learning One Step Closer to the Real World
   Vinitsky et al.. NeurIPS 2022.
3. GPUDrive: Data-Driven, Multi-Agent Driving Simulation at 1 Million FPS
   Cornelisse et al.. arXiv 2024.
4. SMARTS: Scalable Multi-Agent RL Training School for Autonomous Driving
   Zhou et al.. CoRL 2020.
5. Learning by Cheating
   Chen et al.. CoRL 2019.
6. End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
   Zhang et al.. ICCV 2021.
7. InterSim: Interactive Traffic Simulation via Explicit Relation Modeling
   Sun et al.. IROS 2022.
8. CARLA: An Open Urban Driving Simulator
   Dosovitskiy et al.. CoRL 2017.
9. Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
   Gulino et al.. NeurIPS 2023.