ReinVBC：一种基于模型的强化学习方法用于车辆制动控制器
ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
AI总结：
本文提出ReinVBC，利用基于模型的强化学习方法解决车辆制动控制问题，通过工程设计获得可靠的车辆动力学模型和制动策略，实验证明其在实际车辆制动中的有效性及替代传统防抱死刹车系统潜力。
摘要：
制动系统是确保当前车辆安全性和操控性的关键模块，其在生产过程中依赖于大量的手动校准。减少劳动和时间消耗同时保持车辆制动控制器（VBC）性能对车辆行业有益。基于模型的离线强化学习方法，通过数据驱动的动力学模型促进策略探索，为解决现实控制任务提供了有前景的解决方案。本文提出ReinVBC，将其应用于车辆制动控制问题。我们引入有用的工程设计到模型学习和利用范式中，以获得可靠的车辆动力学模型和有能力的制动策略。几个结果展示了我们方法在现实车辆制动中的能力及其替代生产级防抱死刹车系统的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
