Manipulator Motion Planning via Centralized Training and Decentralized Execution Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

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Manipulator Motion Planning via Centralized Training and Decentralized Execution Multi-Agent Reinforcement Learning

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Advanced Robotics and Mechatronics (ICARM)
language:
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Author:

Wang Yuliu¹,Sagawa Ryusuke²

Affiliation:

1. University of Tsukuba, 1-1-1 Tennodai,Intelligent and Mechanical Interaction System,Tsukuba,Ibaraki,Japan,305-8577

2. Computer Vision Research Team, Artificial Intelligence Research Center(AIRC-CVRT), The National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono,Tsukuba,Ibaraki,Japan,305-8577

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9959088/9959097/09959550.pdf?arnumber=9959550

Reference24 articles.

1. Model learning for robot control: a survey

2. Reinforcement Learning: An Introduction

3. Redundant Robot Control Using Multi Agent Reinforcement Learning

4. Proximal Policy Optimization Algorithms;schulman;arXiv preprint arXiv 1707 06347,2017

Cited by 2 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

1. MACNS: A generic graph neural network integrated deep reinforcement learning based multi-agent collaborative navigation system for dynamic trajectory planning;Information Fusion;2024-05

2. A Hierarchical Robot Learning Framework for Manipulator Reactive Motion Generation via Multi-Agent Reinforcement Learning and Riemannian Motion Policies;IEEE Access;2023

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