mlsanityData to 7 Oct 2026

A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving

arXiv 2304.08235 ↗First posted 14 Apr 2023cs.LG cs.AI cs.ROcited by 7

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

Abstract (arXiv)

Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still challenging due to the significant discrepancies between simulation and reality. To address this issue, we propose a robust DRL framework that leverages platform-dependent perception modules to extract task-relevant information and train a lane-following and overtaking agent in simulation. This framework facilitates the seamless transfer of the DRL agent to new simulated environments and the real world with minimal effort. We evaluate the performance of the agent in various driving scenarios in both simulation and the real world, and compare it to human players and the PID baseline in simulation. Our proposed framework significantly reduces the gaps between different platforms and the Sim2Real gap, enabling the trained agent to achieve similar performance in both simulation and the real world, driving the vehicle effectively.

Key facts. Cited by 7 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 25 of its 63 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

25 of 63 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Modified DDPG car-following model with a real-world human driving experience with CARLA simulator29 Dec

Other references · 24

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  6. 2018Learning Dexterous In-Hand Manipulationcited by 643
  7. 2015Policy Distillationcited by 414
  8. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  9. 2016Learning to reinforcement learncited by 369
  10. 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 351
  11. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  12. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 277
12 more
  1. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 203
  2. 2018An LSTM Network for Highway Trajectory Predictioncited by 81
  3. 2021Memory-based Deep Reinforcement Learning for POMDPscited by 53
  4. 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 44
  5. 2022NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fieldscited by 41
  6. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  7. 2021Learning Efficient Navigation in Vortical Flow Fieldscited by 26
  8. 2019Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfercited by 24
  9. 2020Embodied Visual Navigation with Automatic Curriculum Learning in Real Environmentscited by 20
  10. 2022An Ethical Trajectory Planning Algorithm for Autonomous Vehiclescited by 15
  11. 2020Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real Worldcited by 12
  12. 2023Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweightingcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 4.

7 papers.

  1. 2024Autonomous Driving Small-Scale Cars: A Survey of Recent Developmentcited by 6
  2. 2023Vision-based DRL Autonomous Driving Agent with Sim2Real Transfercited by 2
  3. 2024AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applicationscited by 1
  4. 2026Adaptive Control in Autonomous Driving via Real-Time Recurrent RLcited by 0
  5. 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Drivingcited by 0
  6. 2026Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Drivingcited by 0
  7. 2026A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platformcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  2. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  3. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  4. 2021A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
  5. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  6. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving
  7. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Driving
  8. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles