ReinVBC: モデルベース強化学習による車両ブレーキ制御
ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
オフラインのモデルベース強化学習を用いて、実車のブレーキ制御を学習し、生産グレードのABSに匹敵する性能を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Haoxin Lin, Junjie Zhou, Daheng Xu, Yang Yu
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
