Constrained Reinforcement Learning for Dynamic Material Handling-Reference-Cited by-同舟云学术

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Constrained Reinforcement Learning for Dynamic Material Handling

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Hu Chengpeng¹,Wang Ziming¹,Liu Jialin²,Wen Junyi³,Mao Bifei³,Yao Xin²

Affiliation:

1. Research Institute of Trustworthy Autonomous Systems (RITAS), Southern University of Science and Technology,Shenzhen,China

2. Southern University of Science and Technology,Guangdong Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China

3. Huawei Technologies Co., Ltd,Trustworthiness Theory Research Center,Shenzhen,China

Funder

National Natural Science Foundation of China

Shenzhen Fundamental Research Program

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191999.pdf?arnumber=10191999

Reference37 articles.

1. A state-of-the-art survey of dispatching rules for manufacturing job shop operations

2. A survey of dynamic scheduling in manufacturing systems

3. IPO: Interior-Point Policy Optimization under Constraints

4. A multi-agent based approach to dynamic scheduling with flexible processing capabilities

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