mlsanityData to 14 Oct 2026

Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing

arXiv 2607.20973 ↗First posted 23 Jul 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dynamic limits. Different from lap-time minimization, we formulate defense as a spatial occupancy regulation problem via a hierarchical reinforcement-learning guided model predictive control framework. A Soft Actor-Critic strategic layer operates in the Frenet domain to generate geometry-aware defensive references, which are embedded into the nonlinear model predictive control formulation as spatial regularization under friction constraints. Evaluated on the Thunderhill West circuit in simulation, the framework increases average overtake time from 8.8 s to 14.6 s while significantly reducing opponent progress. Meanwhile, it allows the vehicle to utilize 83.4% of available tire force. The framework achieves a 33.3 ms mean solve time (13.9 ms std), supporting real-time high-speed adversarial interaction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 21 references that are in the map. First posted on arXiv 23 Jul 2026.

Builds on

8 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making18 Dec

Other references · 7

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
  3. 2024Blending Data-Driven Priors in Dynamic Gamescited by 6
  4. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Datacited by 6
  5. 2024Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactionscited by 3
  6. 2025ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learningcited by 3
  7. 2025Fair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systemscited by 1

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025On learning racing policies with reinforcement learning
  2. 2024Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios
  3. 2025Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods
  4. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  5. 2026Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints
  6. 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
  7. 2021Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
  8. 2026Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond