Safe Multiagent Learning With Soft Constrained Policy Optimization in Real Robot Control-Reference-Cited by-同舟云学术

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Safe Multiagent Learning With Soft Constrained Policy Optimization in Real Robot Control

Published:2024-09 Issue:9 Volume:20 Page:10706-10716
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Gu Shangding¹^ORCID,Huang Dianye¹^ORCID,Wen Muning²^ORCID,Chen Guang³^ORCID,Knoll Alois¹^ORCID

Affiliation:

1. School of Computation, Information and Technology, Technical University of Munich, Garching, Germany

2. School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

3. Department of Computer Science and Technology, Tongji University, Shanghai, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10666802/10530650.pdf?arnumber=10530650

Reference37 articles.

1. DIP-QL: A Novel Reinforcement Learning Method for Constrained Industrial Systems

2. A review of safe reinforcement learning: Methods, theory and applications;Gu,2022

3. Updet: Universal multi-agent RL via policy decoupling with transformers;Hu,2020

4. Policy diagnosis via measuring role diversity in cooperative multi-agent RL;Hu,2022

5. A human-centered safe robot reinforcement learning framework with interactive behaviors

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