自律ロボットのための適応的経路計画：UCH強化Q学習アプローチ
Adaptive Path-Planning for Autonomous Robots: A UCH-Enhanced Q-Learning Approach
Qテーブル初期化を最適化するPACOと、動的調整パラメータを持つUCH機構で報酬関数を改善し、Q学習の探索効率と収束速度を高めるIQLフレームワークを提案した。
著者: Wei Liu, Ruiyang Wang, Haonan Wang, Guangwei Liu
分類: cs.RO
原文アブストラクト
Q-learning methods are widely used in robot path planning but often face challenges of inefficient search and slow convergence. We propose an Improved Q-learning (IQL) framework that enhances standard Q-learning in two significant ways. First, we introduce the Path Adaptive Collaborative Optimization (PACO) algorithm to optimize Q-table initialization, providing better initial estimates and accelerating learning. Second, we incorporate a Utility-Controlled Heuristic (UCH) mechanism with dynamically tuned parameters to optimize the reward function, enhancing the algorithm's accuracy and effectiveness in path-planning tasks. Extensive experiments in three different raster grid environments validate the superior performance of our IQL framework. The results demonstrate that our IQL algorithm outperforms existing methods, including FIQL, PP-QL-based CPP, DFQL, and QMABC algorithms, in terms of path-planning capabilities.
