改进Q-learning，让机器人更快找到最优路径
Adaptive Path-Planning for Autonomous Robots: A UCH-Enhanced Q-Learning Approach
- 用PACO算法优化初始化，提升学习起始效率
- 引入动态调节的UCH机制，使奖励函数更精准
- 在三种网格环境中均优于传统方法，适合路径规划场景
Q-learning广泛用于机器人路径规划，但常面临搜索效率低、收敛慢的问题。本文提出改进型Q-learning（IQL）框架，在两个方面增强标准Q-learning：首先引入路径自适应协同优化（PACO）算法优化Q表初始化，提供更优初始估计，加速学习过程；其次融合可调参数的效用控制启发式（UCH）机制，优化奖励函数，提升算法在路径规划任务中的准确性和有效性。在三种不同栅格环境下的大量实验验证了IQL框架的优越性能。结果表明，该算法在路径规划能力上优于现有方法，包括FIQL、PP-QL-based CPP、DFQL和QMABC算法。
原文摘要 · Abstract (English)
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.
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