mlsanityData to 7 Oct 2026

DriverGym: Democratising Reinforcement Learning for Autonomous Driving

arXiv 2111.06889 ↗First posted 12 Nov 2021cs.LG cs.CVcited by 23

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating the RL policies on real-world data. We propose DriverGym, an open-source OpenAI Gym-compatible environment specifically tailored for developing RL algorithms for autonomous driving. DriverGym provides access to more than 1000 hours of expert logged data and also supports reactive and data-driven agent behavior. The performance of an RL policy can be easily validated on real-world data using our extensive and flexible closed-loop evaluation protocol. In this work, we also provide behavior cloning baselines using supervised learning and RL, trained in DriverGym. We make DriverGym code, as well as all the baselines publicly available to further stimulate development from the community.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 11 of its 17 references that are in the map. First posted on arXiv 12 Nov 2021.

Builds on

11 of 17 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  6. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  7. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 239
  8. 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
  9. 2021SimNet: Learning Reactive Self-driving Simulations from Real-world Observationscited by 77
  10. 2019Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Taskscited by 56
  11. 2020CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous drivingcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 5; 2024: 6; 2025: 3; 2026: 3.

23 papers.

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  1. 2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 158
  2. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
  3. 2023The Waymo Open Sim Agents Challengecited by 86
  4. 2022Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real worldcited by 56
  5. 2024BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Predictioncited by 49
  6. 2024GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPScited by 43
  7. 2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
  8. 2022Sample Efficient Deep Reinforcement Learning via Uncertainty Estimationcited by 26
  9. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  10. 2024MetaUrban: An Embodied AI Simulation Platform for Urban Micromobilitycited by 19
  11. 2025Towards Autonomous Micromobility through Scalable Urban Simulationcited by 16
  12. 2026BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Drivingcited by 9
11 more
  1. 2022RITA: Boost Driving Simulators with Realistic Interactive Traffic Flowcited by 7
  2. 2023FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusioncited by 7
  3. 2025UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videoscited by 5
  4. 2024Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Reviewcited by 3
  5. 2022ApolloRL: a Reinforcement Learning Platform for Autonomous Drivingcited by 2
  6. 2024GPD-1: Generative Pre-training for Drivingcited by 2
  7. 2022Query The Agent: Improving sample efficiency through epistemic uncertainty estimationcited by 1
  8. 2024Hybrid Imitation-Learning Motion Planner for Urban Drivingcited by 1
  9. 2025TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learningcited by 0
  10. 2026MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Drivingcited by 0
  11. 2026DreamStream: Towards Policy-Oriented Generative Simulation for End-to-End Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
  2. 2026Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
  3. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  4. 2020Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
  5. 2025AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
  6. 2024Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
  7. 2025V-Max: A Reinforcement Learning Framework for Autonomous Driving
  8. 2025AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning