Research on Control Strategy of Manipulator Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Research on Control Strategy of Manipulator Based on Deep Reinforcement Learning

Published:2023-07-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms
language:
Short-container-title:

Author:

Xu Nuo¹^ORCID,Shao Yanpeng¹^ORCID,Fan Xiaoyan¹^ORCID

Affiliation:

1. National Demonstration Center for Experimental Mechanical and Electrical Engineering Educatio, Tianjin University of Technology, China and Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, Tianjin University of Technology, China

Funder

The Key Project of the 2022 College Students Innovation and Entrepreneurship Training Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3611450.3611457

Reference39 articles.

1. Correlation between Bond-Length Change and Vibrational Frequency Shift in Hydrogen-Bonded Complexes Revisited

2. A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

3. Correlation between Bond-Length Change and Vibrational Frequency Shift in Hydrogen-Bonded Complexes Revisited

4. Reducing the Dimensionality of Data with Neural Networks

5. Performance and Scalability of GPU-Based Convolutional Neural Networks

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