mlsanityData to 14 Oct 2026

Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test

arXiv 2402.17050 ↗First posted 26 Feb 2024eess.SY cs.RO cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Key facts. Cited by 1 paper 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 24 of its 62 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

24 of 62 references.

References · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016End to End Learning for Self-Driving Carscited by 1,009
  3. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  4. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  5. 2017Robust Adversarial Reinforcement Learningcited by 451
  6. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 397
  7. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  8. 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 337
  9. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 286
  10. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 158
  11. 2019Planning with Goal-Conditioned Policiescited by 149
  12. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
12 more
  1. 2017Dissipation of stop-and-go waves via control of autonomous vehicles: Field experimentscited by 45
  2. 2023I-24 MOTION: An instrument for freeway traffic sciencecited by 26
  3. 2018Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehiclescited by 19
  4. 2021Cooperative Autonomous Vehicles that Sympathize with Human Driverscited by 18
  5. 2021Limitations and Improvements of the Intelligent Driver Model (IDM)cited by 17
  6. 2019Are commercially implemented adaptive cruise control systems string stable?cited by 13
  7. 2022Automatic vehicle trajectory data reconstruction at scalecited by 8
  8. 2023So you think you can track?cited by 5
  9. 2023Cooperative Driving for Speed Harmonization in Mixed-Traffic Environmentscited by 4
  10. 2023The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle trackingcited by 3
  11. 2023Traffic smoothing using explicit local controllerscited by 2
  12. 2023Online Min Cost Circulation for Multi-Object Tracking on Fragmentscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176

Similar, not cited

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  1. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  2. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  3. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  4. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  5. 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
  6. 2024Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
  7. 2024Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
  8. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey