mlsanityData to 7 Oct 2026

ApolloRL: a Reinforcement Learning Platform for Autonomous Driving

arXiv 2201.12609 ↗First posted 29 Jan 2022cs.RO cs.LG stat.MLcited by 2

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

Abstract (arXiv)

We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving. The platform provides a complete closed-loop pipeline with training, simulation, and evaluation components. It comes with 300 hours of real-world data in driving scenarios and popular baselines such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents. We elaborate in this paper on the architecture and the environment defined in the platform. In addition, we discuss the performance of the baseline agents in the ApolloRL environment.

Key facts. Cited by 2 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 16 of its 26 references that are in the map. First posted on arXiv 29 Jan 2022.

Builds on

16 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021DriverGym: Democratising Reinforcement Learning for Autonomous Driving12 Nov

Other references · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  5. 2016OpenAI Gymcited by 1,684
  6. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  7. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  8. 2018Learning Dexterous In-Hand Manipulationcited by 643
  9. 2018Behavioral Cloning from Observationcited by 482
  10. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  11. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
  12. 2017DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Carscited by 249
3 more
  1. 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
  2. 2018Learning to Navigate in Cities Without a Mapcited by 103
  3. 2019Cross-View Policy Learning for Street Navigationcited by 11

Built on by

Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learningcited by 4
  2. 2023Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  2. 2022Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
  3. 2026PlanRL: A Trajectory Planning Architecture for Reinforcement Learning-based Driving Experts
  4. 2025RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
  5. 2024AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset
  6. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  7. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  8. 2024Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study