A Critical Review of Reinforcement Learning Algorithms for Mobile Robot Path Planning

ABSTRACT Mobile robots are increasingly deployed across industrial and service sectors, where autonomous navigation is required in both time‐invariant, static environments and time‐variant, dynamic environments. During navigation, robots must handle diverse obstacles, including fixed and moving objects, while minimizing travel distance, execution time, and collision risk. Although various machine‐learning‐based path planning approaches have been proposed to address these challenges, many depend on pre‐collected data sets, and obtaining such data in real‐time, unpredictable environments is difficult and often impractical. This review focuses on reinforcement‐learning‐based path planning, wherein mobile robots learn obstacle characteristics, path structure, and optimal policies directly from the environment through trial‐and‐error interaction, largely without relying on external training data. The study examines key challenges associated with autonomous navigation and analyzes reinforcement learning techniques in terms of their advantages, limitations, applications, performance metrics, obstacle categories, and obstacle avoidance mechanisms. A quantitative assessmen