Affiliation:
1. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
2. Department of Electronic and Electrical Engineering, University of Straclyde, Glasglow G1 1XW, UK
Abstract
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference157 articles.
1. Industry 4.0;Lasi;Bus. Inf. Syst. Eng.,2014
2. Sigov, A., Ratkin, L., Ivanov, L.A., and Xu, L.D. (2022). Emerging enabling technologies for Industry 4.0 and beyond. Inf. Syst. Front., 1–11.
3. Hua, J., Zeng, L., Li, G., and Ju, Z. (2021). Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning. Sensors, 21.
4. Toward Robotic Manipulation;Mason;Annu. Rev. Control. Robot. Auton. Syst.,2018
5. Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks;Hafiz;Comput. Syst. Sci. Eng.,2023
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献