Applying the learning mechanism of natural living beings to endow intelligent robots with humanoid perception and decision-making wisdom becomes an important force to promote the revolution of science and technology in robot domains. Advances in reinforcement learning (RL) over the past decades have led robotics to be highly automated and intelligent, which ensures safety operation instead of manual work and implementation of more intelligence for many challenging tasks. As an important branch of machine learning, RL can realize sequential decision-making under uncertainties through end-to-end learning and has made a series of significant breakthroughs in robot applications. In this review article, we cover RL algorithms from theoretical background to advanced learning policies in different domains, which accelerate to solving practical problems in robotics. The challenges, open issues, and our thoughts on future research directions of RL are also presented to discover new research areas with the objective to motivate new interest.

应用自然生物的学习机制，赋予智能机器人类人感知和决策智慧，成为推动机器人领域科技革命的重要力量。过去几十年强化学习 (RL) 的进步使机器人技术变得高度自动化和智能化，从而确保安全操作而不是手动工作，并为许多具有挑战性的任务实施更多智能。作为机器学习的一个重要分支，强化学习可以通过端到端的学习实现不确定性下的顺序决策，并在机器人应用方面取得了一系列重大突破。在这篇评论文章中，我们涵盖了从理论背景到不同领域的高级学习策略的 RL 算法，加速解决机器人技术中的实际问题。还提出了挑战、未解决的问题以及我们对 RL 未来研究方向的想法，以发现新的研究领域，以激发新的兴趣。