認定アクター・クリティック：制御バリア関数を用いた安全ナビゲーションのための階層強化学習
Certificated Actor-Critic: Hierarchical Reinforcement Learning with Control Barrier Functions for Safe Navigation
制御バリア関数（CBF）を報酬設計に取り入れた階層型強化学習アルゴリズムを提案し、安全なナビゲーションを実現する。理論解析とシミュレーションで有効性を示した。
著者: Junjun Xie, Shuhao Zhao, Liang Hu, Huijun Gao
分類: cs.RO, cs.LG
原文アブストラクト
Control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and they rely on simplified system models; conversely, the learning-based methods suffer from the lack of quantitative indication in terms of navigation performance and safety. In this paper, we present a new model-free reinforcement learning algorithm called Certificated Actor-Critic (CAC), which introduces a hierarchical reinforcement learning framework and well-defined reward functions derived from CBFs. We carry out theoretical analysis and proof of our algorithm, and propose several improvements in algorithm implementation. Our analysis is validated by two simulation experiments, showing the effectiveness of our proposed CAC algorithm.
