mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Autonomous Driving

arXiv 1811.11329 ↗First posted 28 Nov 2018cs.CV cs.LG cs.ROcited by 36

Not on the map. Why?

Abstract (arXiv)

Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. Moreover, the autonomous driving vehicles must also keep functional safety under the complex environments. To deal with these challenges, we first adopt the deep deterministic policy gradient (DDPG) algorithm, which has the capacity to handle complex state and action spaces in continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our environment to avoid physical damage. Meanwhile, we select a set of appropriate sensor information from TORCS and design our own rewarder. In order to fit DDPG algorithm to TORCS, we design our network architecture for both actor and critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We evaluate on different modes in TORCS and show both quantitative and qualitative results.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 3; 2024: 6; 2025: 6; 2026: 2.

36 papers.

Sort:

By area:

  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  2. 2024Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradientcited by 51
  3. 2022Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Modelcited by 45
  4. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 32
  5. 2021Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safetycited by 30
  6. 2021Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patternscited by 13
  7. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  8. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policiescited by 10
  9. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Drivingcited by 9
  10. 2021Objective-aware Traffic Simulation via Inverse Reinforcement Learningcited by 8
  11. 2025HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agentscited by 6
  12. 2023Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Modelscited by 5
24 more
  1. 2023A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospectscited by 5
  2. 2022Optimizing Trajectories for Highway Driving with Offline Reinforcement Learningcited by 4
  3. 2022CtrlFormer: Learning Transferable State Representation for Visual Control via Transformercited by 4
  4. 2021Goal-constrained Sparse Reinforcement Learning for End-to-End Drivingcited by 3
  5. 2022ARLO: A Framework for Automated Reinforcement Learningcited by 3
  6. 2022GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Drivingcited by 3
  7. 2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
  8. 2022Safe Inverse Reinforcement Learning via Control Barrier Functioncited by 3
  9. 2025Generative AI for Autonomous Driving: A Reviewcited by 3
  10. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environmentcited by 2
  11. 2024Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learningcited by 2
  12. 2021Controlling an Inverted Pendulum with Policy Gradient Methods-A Tutorialcited by 1
  13. 2023Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Managementcited by 1
  14. 2025Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Controlcited by 1
  15. 2026BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Drivingcited by 1
  16. 2021Integrating Imitation Learning with Human Driving Data into Reinforcement Learning to Improve Training Efficiency for Autonomous Drivingcited by 0
  17. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learningcited by 0
  18. 2024Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Managementcited by 0
  19. 2024ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectoriescited by 0
  20. 2024Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speedcited by 0
  21. 2025Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baselinecited by 0
  22. 2025Hierarchical Deep Deterministic Policy Gradient for Autonomous Maze Navigation of Mobile Robotscited by 0
  23. 2025Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settingscited by 0
  24. 2026CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recoverycited by 0