نتایج متقاضیان استادراهنما در مرکز هوش مصنوعی و یادگیری عمیق ایران

Abstract—Reinforcement learning combined with neural networks has recently led to a wide range of successes in learning policies in different domains. For robot manipulation, reinforcement learning algorithms bring the hope for machines to have the human-like abilities by directly learning dexterous manipulation from raw pixels. In this review paper, we address the current status of reinforcement learning algorithms used in the field. We also cover essential theoretical background and main issues with current algorithms, which are limiting their applications of reinforcement learning algorithms in solving practical problems in robotics. We also share our thoughts on a number of future directions for reinforcement learning research.

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least 30 papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.