Reinforcement learning for mobile robot mapless navigation: a review of recent advances

Abstract Mobile robot navigation remains a critical challenge in robotics, with applications spanning autonomous vehicles, search and rescue, and other dynamic environments. In recent years, reinforcement learning (RL) has become a powerful approach for solving complex tasks such as robotic manipulation, gameplay, and autonomous driving. By enabling robots to learn optimal navigation strategies through interaction with their environment, RL offers a promising pathway to autonomous mobility. This review presents a comprehensive overview of recent advancements in RL as applied to mobile robot navigation. We begin by outlining core RL concepts, agents, environments, rewards, and value functions, explaining their roles in navigation. Key RL techniques, including Q-learning, deep reinforcement learning, Markov Decision Processes (MDPs), and policy gradient methods, are examined to highlight their transformative impact on navigation performance. The review also explores a range of practical applications and identifies current challenges and open research directions. Critical issues such as safety, sample efficiency, and scalability to real-world scenarios are discussed in depth to ensure robust deployment of RL-based systems. Lastly, this review synthesizes the state-of-the-art in reinforcement learning for mobile robot navigation, offering readers both a foundational understanding and valuable insights into emerging trends and future opportunities in this rapidly evolving field.