制御バリア関数の学習と強化学習への応用：サーベイ
Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey
強化学習の安全性を保証する制御バリア関数に注目し、その自動学習手法と安全強化学習への応用に関する既存研究を体系的に調査したサーベイ論文。
著者: Maeva Guerrier, Hassan Fouad, Giovanni Beltrame
分類: cs.LG, cs.AI, cs.RO, cs.SY, eess.SY
原文アブストラクト
Reinforcement learning is a powerful technique for developing new robot behaviors. However, typical lack of safety guarantees constitutes a hurdle for its practical application on real robots. To address this issue, safe reinforcement learning aims to incorporate safety considerations, enabling faster transfer to real robots and facilitating lifelong learning. One promising approach within safe reinforcement learning is the use of control barrier functions. These functions provide a framework to ensure that the system remains in a safe state during the learning process. However, synthesizing control barrier functions is not straightforward and often requires ample domain knowledge. This challenge motivates the exploration of data-driven methods for automatically defining control barrier functions, which is highly appealing. We conduct a comprehensive review of the existing literature on safe reinforcement learning using control barrier functions. Additionally, we investigate various techniques for automatically learning the Control Barrier Functions, aiming to enhance the safety and efficacy of Reinforcement Learning in practical robot applications.
