適応的プランナパラメータ調整のための強化学習：階層アーキテクチャの視点
Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture
強化学習によるパラメータ調整を低頻度の調整・中頻度の計画・高頻度の制御という階層構造に組み込み、シミュレーションと実環境で既存手法を上回りBARNチャレンジで優勝した。
著者: Lu Wangtao, Wei Yufei, Xu Jiadong, Jia Wenhao, Li Liang, Xiong Rong, Wang Yue
分類: cs.RO
原文アブストラクト
Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a more structured approach. In this paper, we propose a hierarchical architecture for reinforcement learning-based parameter tuning. The architecture introduces a hierarchical structure with low-frequency parameter tuning, mid-frequency planning, and high-frequency control, enabling concurrent enhancement of both upper-layer parameter tuning and lower-layer control through iterative training. Experimental evaluations in both simulated and real-world environments show that our method surpasses existing parameter tuning approaches. Furthermore, our approach achieves first place in the Benchmark for Autonomous Robot Navigation (BARN) Challenge.