A Platform for Deploying Multi-agent Deep Reinforcement Learning in Microgrid Distributed Control

Mapping Intimacies ◽

10.1109/pesgm46819.2021.9638039 ◽

2021 ◽

Keyword(s):

Reinforcement Learning ◽

Distributed Control ◽

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A Distributed Control Method for Urban Networks Using Multi-Agent Reinforcement Learning Based on Regional Mixed Strategy Nash-Equilibrium

IEEE Access ◽

10.1109/access.2020.2968937 ◽

2020 ◽

Vol 8 ◽

pp. 19750-19766 ◽

Author(s):

Zhaowei Qu ◽

Zhaotian Pan ◽

Yongheng Chen ◽

Xin Wang ◽

Haitao Li

Keyword(s):

Reinforcement Learning ◽

Nash Equilibrium ◽

Distributed Control ◽

Mixed Strategy ◽

Control Method ◽

Urban Networks ◽

Mixed Strategy Nash Equilibrium ◽

Multi Agent ◽

Strategy Nash Equilibrium

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A Distributed Control in Islanded DC Microgrid based on Multi-Agent Deep Reinforcement Learning

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon43393.2020.9254716 ◽

2020 ◽

Author(s):

Yang Xia ◽

Yan Xu ◽

Yu Wang ◽

Souvik Dasgupta

Keyword(s):

Reinforcement Learning ◽

Distributed Control ◽

Dc Microgrid ◽

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Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

10.1061/9780784483053.039 ◽

2020 ◽

Author(s):

Yang Zhao ◽

Jian-Ming Hu ◽

Ming-Yang Gao ◽

Zuo Zhang

Keyword(s):

Reinforcement Learning ◽

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

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An Improved Distributed Control on Output Regulation for Heterogeneous Multi-Agent Systems

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8482711 ◽

2018 ◽

Author(s):

Ji-Lie Zhang ◽

Tao Feng ◽

Fei Yan ◽

Yiduo Zhou

Keyword(s):

Distributed Control ◽

Output Regulation ◽

Multi Agent Systems ◽

Agent Systems ◽

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Hierarchical Distributed Control Design for Multi-agent Systems Using Approximate Simulation

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2013.00868 ◽

2014 ◽

Vol 39 (6) ◽

pp. 868-874 ◽

Author(s):

Yu-Tao TANG ◽

Yi-Guang HONG

Keyword(s):

Distributed Control ◽

Control Design ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent ◽

Approximate Simulation

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Output feedback reinforcement learning based optimal output synchronisation of heterogeneous discrete-time multi-agent systems

IET Control Theory and Applications ◽

10.1049/iet-cta.2018.6266 ◽

2019 ◽

Vol 13 (17) ◽

pp. 2866-2876

Author(s):

Syed Ali Asad Rizvi ◽

Zongli Lin

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Output Feedback ◽

Multi Agent Systems ◽

Agent Systems ◽

Optimal Output ◽

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Distributed Control-Estimation Synthesis for Stochastic Multi-Agent Systems via Virtual Interaction between Non-neighboring Agents

IEEE Control Systems Letters ◽

10.1109/lcsys.2021.3086848 ◽

2021 ◽

pp. 1-1

Author(s):

Hojin Lee ◽

Cheolhyeon Kwon

Keyword(s):

Distributed Control ◽

Multi Agent Systems ◽

Agent Systems ◽

Virtual Interaction ◽

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Multi-agent deep reinforcement learning with type-based hierarchical group communication

Applied Intelligence ◽

10.1007/s10489-020-02065-9 ◽

2021 ◽

Author(s):

Hao Jiang ◽

Dianxi Shi ◽

Chao Xue ◽

Yajie Wang ◽

Gongju Wang ◽

...

Keyword(s):

Reinforcement Learning ◽

Group Communication ◽

Multi Agent ◽

Hierarchical Group

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Multi-Agent Deep Reinforcement Learning Based Cooperative Edge Caching for Ultra-Dense Next-Generation Networks

IEEE Transactions on Communications ◽

10.1109/tcomm.2020.3044298 ◽

2020 ◽

pp. 1-1

Author(s):

Shuangwu Chen ◽

Zhen Yao ◽

Xiaofeng Jiang ◽

Jian Yang ◽

Lajos Hanzo

Keyword(s):

Reinforcement Learning ◽

Next Generation Networks ◽

Next Generation ◽

Multi Agent ◽

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Coordinated Ramp Metering Control Based on Multi-Agent Reinforcement Learning

2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) ◽

10.1109/yac51587.2020.9337711 ◽

2020 ◽

Author(s):

Jiyuan Tan ◽

Qianqian Qiu ◽

Weiwei Guo

Keyword(s):

Reinforcement Learning ◽

Ramp Metering ◽

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