mlsanityData to 14 Oct 2026

Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representations

arXiv 2609.19894 ↗First posted 17 Sep 2026cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

Parking is a routine yet safety-critical task for autonomous vehicles operating in urban environments. However, cluttered and weakly structured parking spaces, compounded by the interactive uncertainty from surrounding vehicles, hinder reliable maneuver generation. To address these challenges, we develop a waypoint-level offline reinforcement learning framework for interaction-aware autonomous parking. Specifically, a dedicated parking dataset is constructed from hierarchical expert rollouts with rotational waypoint augmentation, covering both non-interactive scenarios and interactive ones. The policy is then conditioned on a compact state representation, in which LiDAR-based obstacle features are adapted to the target pose via feature-wise linear modulation. A state-conditioned tokenizer further quantizes continuous waypoint sequences into discrete action tokens, over which conservative Q-learning is performed to suppress value overestimation on poorly supported actions. Extensive closed-loop experiments are conducted in the high-fidelity CARLA simulator. The proposed framework attains the highest parking success rate among all baselines and transfers reliably to unseen parking slots.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 18 of its 30 references that are in the map. First posted on arXiv 17 Sep 2026.

Builds on

18 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios21 Dec

Other references · 17

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  2. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  4. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  5. 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 302
  6. 2025NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidancecited by 49
  7. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 47
  8. 2023DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Drivingcited by 46
  9. 2023Action-Quantized Offline Reinforcement Learning for Robotic Skill Learningcited by 36
  10. 2021Off-Policy Reinforcement Learning with Delayed Rewardscited by 25
  11. 2021Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigationcited by 22
  12. 2024ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planningcited by 15
5 more
  1. 2022Behavior Prior Representation learning for Offline Reinforcement Learningcited by 12
  2. 2024HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarioscited by 11
  3. 2022Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planningcited by 5
  4. 2025MultiPark: Multimodal Parking Transformer with Next-Segment Predictioncited by 4
  5. 2021Neural Motion Planning for Autonomous Parkingcited by 1

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  1. 2025SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
  2. 2026ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation
  3. 2026N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
  4. 2026NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
  5. 2025Reward-Augmented Reinforcement Learning for Continuous Control in Precision Autonomous Parking via Policy Optimization Methods
  6. 2026Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
  7. 2025Automated Parking Trajectory Generation Using Deep Reinforcement Learning
  8. 2026Optimal Reward Shaping: Autonomous Car Parking Case Study