Analyzing the Effects of Reinforcement Learning to Develop Humanoid Robots

Naaima Suroor, Imran Hussain, Aqeel Khalique, T. Khan
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Abstract

Reinforcement learning is a flourishing machine learning concept that has greatly influenced how robots are designed and taught to solve problems without human intervention. Robotics is not an alien discipline anymore, and we have several great innovations in this field that promise to impact lives for the better. However, humanoid robots are still a baffling concept for scientists, although we have managed to develop a few great inventions which look, talk, work, and behave very similarly to humans. But, can these machines actually exhibit the cognitive abilities of judgment, problem-solving, and perception as well as humans? In this article, the authors analyzed the probable impact and aspects of robots and their potential to behave like humans in every possible way through reinforcement learning techniques. The paper also discusses the gap between 'natural' and 'artificial' knowledge.
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强化学习在仿人机器人开发中的作用分析
强化学习是一个蓬勃发展的机器学习概念,它极大地影响了机器人的设计和教学方式,使其在没有人为干预的情况下解决问题。机器人不再是一门陌生的学科,我们在这个领域有几项伟大的创新,有望改善人们的生活。然而,对于科学家来说,类人机器人仍然是一个令人困惑的概念,尽管我们已经成功地开发了一些伟大的发明,它们的外观、说话、工作和行为都与人类非常相似。但是,这些机器真的能像人类一样表现出判断、解决问题和感知的认知能力吗?在这篇文章中,作者分析了机器人可能的影响和方面,以及它们通过强化学习技术在各种可能的方式上表现得像人类一样的潜力。文章还讨论了“自然”知识和“人工”知识之间的差距。
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