mlsanityData to 7 Oct 2026

Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving

arXiv 2301.13313 ↗First posted 30 Jan 2023cs.LG cs.AI cs.ROcited by 0

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

Abstract (arXiv)

Model Predictive Control (MPC) is attracting tremendous attention in the autonomous driving task as a powerful control technique. The success of an MPC controller strongly depends on an accurate internal dynamics model. However, the static parameters, usually learned by system identification, often fail to adapt to both internal and external perturbations in real-world scenarios. In this paper, we firstly (1) reformulate the problem as a Partially Observed Markov Decision Process (POMDP) that absorbs the uncertainties into observations and maintains Markov property into hidden states; and (2) learn a recurrent policy continually adapting the parameters of the dynamics model via Recurrent Reinforcement Learning (RRL) for optimal and adaptive control; and (3) finally evaluate the proposed algorithm (referred as textitMPC-RRL) in CARLA simulator and leading to robust behaviours under a wide range of perturbations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 25 references that are in the map. First posted on arXiv 30 Jan 2023.

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13 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022ApolloRL: a Reinforcement Learning Platform for Autonomous Driving29 Jan

Other references · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 457
  5. 2016Learning to reinforcement learncited by 369
  6. 2018Differentiable MPC for End-to-end Planning and Controlcited by 178
  7. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 55
  8. 2016Automatic LQR Tuning Based on Gaussian Process Global Optimizationcited by 29
  9. 2017Goal-Driven Dynamics Learning via Bayesian Optimizationcited by 19
  10. 2019Deep Value Model Predictive Controlcited by 14
  11. 2021On Recurrent Neural Networks for learning-based control: recent results and ideas for future developmentscited by 14
  12. 2020Learning High-Level Policies for Model Predictive Controlcited by 8

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  3. 2026Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study
  4. 2024Faster Model Predictive Control via Self-Supervised Initialization Learning
  5. 2025Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
  6. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  7. 2025A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
  8. 2026Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning