A review on reinforcement deep learning in robotics

Hare Shankar Kumhar, V. Kukshal
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Abstract

Since the world is experiencing the industrial revolution 4.0, robotics is one of the many instruments that is making a significant effect. Reinforcement Learning (RL) has been emerged as one of the promising techniques in recent years to significantly improve control over this technological wonder. RL allows robots to become self-aware and self-directed toward completing a certain goal, which is then followed by user actions. This scientific field has seen multiple significant advancements over decades of hard work, and it will continue to do so in the future. As a result, this paper fills a need in the scientific community by providing a systematic assessment of research papers published in the last decade. In relation to the study issue, this paper raises and answers several relevant questions. Future scholars will have a good understanding of RL-based robotics after reading this study, which they will be able to apply into their own research.
机器人强化深度学习研究综述
由于世界正在经历工业革命4.0,机器人是产生重大影响的众多工具之一。近年来,强化学习(RL)已成为一种有前途的技术,可以显着改善对这一技术奇迹的控制。强化学习允许机器人变得自我意识和自我导向,以完成特定的目标,然后由用户操作。经过几十年的努力,这一科学领域取得了多项重大进展,未来还将继续如此。因此,这篇论文通过对过去十年发表的研究论文进行系统评估,填补了科学界的需求。针对研究问题,本文提出并回答了几个相关问题。未来的学者在阅读本研究后将对基于强化学习的机器人有一个很好的理解,他们将能够将其应用到自己的研究中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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