ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving
Abstract (arXiv)
Braking system, the key module to ensure the safety and steer-ability of current vehicles, relies on extensive manual calibration during production. Reducing labor and time consumption while maintaining the Vehicle Braking Controller (VBC) performance greatly benefits the vehicle industry. Model-based methods in offline reinforcement learning, which facilitate policy exploration within a data-driven dynamics model, offer a promising solution for addressing real-world control tasks. This work proposes ReinVBC, which applies an offline model-based reinforcement learning approach to deal with the vehicle braking control problem. We introduce useful engineering designs into the paradigm of model learning and utilization to obtain a reliable vehicle dynamics model and a capable braking policy. Several results demonstrate the capability of our method in real-world vehicle braking and its potential to replace the production-grade anti-lock braking system.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 17 of its 38 references that are in the map. First posted on arXiv 6 Apr 2026.
Builds on
17 of 38 references.
References · 17
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
- 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
- 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
- 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
- 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
- 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
- 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
- 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 365
- 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 297
- 2018Model-Based Value Estimation for Efficient Model-Free Reinforcement Learningcited by 152
5 more
- 2022RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learningcited by 115
- 2021NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learningcited by 63
- 2022Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimizationcited by 16
- 2023Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learningcited by 12
- 2024Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learningcited by 10
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control
- 2026Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study
- 2023Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving
- 2022Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios
- 2023Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
- 2023Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
- 2021UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning
- 2024Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies