How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.

中文翻译：

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CLSQL：基于连续局部搜索策略的移动机器人路径规划改进 Q-Learning 算法

如何快速生成移动机器人的路径规划是机器人领域的一个难题。Q-learning（QL）算法最近越来越多地用于移动机器人路径规划领域。然而，它的选择策略在大多数情况下在早期搜索过程中是盲目的，这会减慢最优解的收敛速度，尤其是在复杂环境中。因此，在本文中，我们提出了一种连续局部搜索 Q-Learning (CLSQL) 算法来解决这些问题并保证规划路径的质量。首先，全球环境逐渐划分为独立的局部环境。然后，使用先验知识在每个局部环境中搜索中间点。之后，实现每个中间点之间的搜索，到达目的地点。最后，