mlsanityData to 7 Oct 2026

Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control

arXiv 2311.04303 ↗First posted 7 Nov 2023eess.SY cs.RO cs.SYcited by 4

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

Abstract (arXiv)

In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints robustification, feasibility, and closed-loop performance. To this end, we conceive an RL agent to proactively anticipate upcoming control tasks and to dynamically determine the most suitable combination of key SNMPC parameters - foremost the robustification factor kappa and the Uncertainty Propagation Horizon (UPH) Tu. We analyze the trained RL agent's decision-making process and highlight its ability to learn context-dependent optimal parameters. One key finding is that adapting the constraints robustification factor with the learned policy reduces conservatism and improves closed-loop performance while adapting UPH renders previously infeasible SNMPC problems feasible when faced with severe disturbances. We showcase the enhanced robustness and feasibility of our Adaptive SNMPC (aSNMPC) through the real-time motion control task of an autonomous passenger vehicle to follow an optimal race line when confronted with significant time-variant disturbances. Experimental findings demonstrate that our look-ahead RL-driven aSNMPC outperforms its Static SNMPC (sSNMPC) counterpart in minimizing the lateral deviation both with accurate and inaccurate disturbance assumptions and even when driving in previously unexplored environments.

Key facts. Cited by 4 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 35 references that are in the map. First posted on arXiv 7 Nov 2023.

Builds on

16 of 35 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  3. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
  4. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 91
  5. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 72
  6. 2019acados: a modular open-source framework for fast embedded optimal controlcited by 61
  7. 2019Probabilistic model predictive safety certification for learning-based controlcited by 20
  8. 2019Stochastic data-driven model predictive control using Gaussian processescited by 19
  9. 2020Reinforcement Learning for Mixed-Integer Problems Based on MPCcited by 11
  10. 2023EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Applicationcited by 10
  11. 2021Reinforcement Learning of the Prediction Horizon in Model Predictive Controlcited by 9
  12. 2019Sample-Based Learning Model Predictive Control for Linear Uncertain Systemscited by 7
4 more
  1. 2021Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learningcited by 7
  2. 2021MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehiclescited by 5
  3. 2023A Stochastic Nonlinear Model Predictive Control with an Uncertainty Propagation Horizon for Autonomous Vehicle Motion Controlcited by 4
  4. 2021Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive controlcited by 1

Built on by

Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 19
  2. 2024A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Controlcited by 3
  3. 2026Generalized Kalman filter based temporal difference reinforcement learningcited by 0
  4. 2026Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPCcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles
  2. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  3. 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation
  4. 2025Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
  5. 2023Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
  6. 2021Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving
  7. 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
  8. 2026Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning