论文标题
机器人技术中的强化学习简洁介绍
A Concise Introduction to Reinforcement Learning in Robotics
论文作者
论文摘要
最大的障碍机器人面孔之一是精致且难以工程行为的方面。强化学习提供了一组工具，以及解决此问题的框架。同时，机器人技术的疑虑为增强学习的进步提供了坚实的测试场和评估指标。这两个学科齐头并进，就像数学和物理学领域一样。通过本调查文件，我们旨在通过关注机器人技术的机车和控制方面的加强学习中的工作来激发两个学科研究社区之间的联系。此外，我们的目标不仅要突出著名的成功，而且要强调在机器人技术中使用强化学习的关键挑战。本文旨在作为应用于机器人技术领域的强化学习研究人员的参考指南。文献调查处于相当介绍性的水平，旨在有抱负的研究人员。适当地，我们涵盖了强化学习领域研究所需的最重要的概念，并考虑了机器人技术。通过对这个问题的彻底分析，我们能够体现如何利用加强学习，并专注于开放式问题，以及未来研究的潜力。
One of the biggest hurdles robotics faces is the facet of sophisticated and hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a framework to address this problem. In parallel, the misgivings of robotics offer a solid testing ground and evaluation metric for advancements in reinforcement learning. The two disciplines go hand-in-hand, much like the fields of Mathematics and Physics. By means of this survey paper, we aim to invigorate links between the research communities of the two disciplines by focusing on the work done in reinforcement learning for locomotive and control aspects of robotics. Additionally, we aim to highlight not only the notable successes but also the key challenges of the application of Reinforcement Learning in Robotics. This paper aims to serve as a reference guide for researchers in reinforcement learning applied to the field of robotics. The literature survey is at a fairly introductory level, aimed at aspiring researchers. Appropriately, we have covered the most essential concepts required for research in the field of reinforcement learning, with robotics in mind. Through a thorough analysis of this problem, we are able to manifest how reinforcement learning could be applied profitably, and also focus on open-ended questions, as well as the potential for future research.
