A coordinated multi-agent reinforcement learning approach to multi-level cache co-partitioning

Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 ◽

10.23919/date.2017.7927098 ◽

2017 ◽

Author(s):

Rahul Jain ◽

Preeti Ranjan Panda ◽

Sreenivas Subramoney

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

Multi Agent ◽

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ReLight-WCTM: Multi-Agent Reinforcement Learning Approach for Traffic Light Control within a Realistic Traffic Simulation

10.1109/tsp52935.2021.9522612 ◽

2021 ◽

Author(s):

Peter Palos ◽

Arpad Huszak

Keyword(s):

Reinforcement Learning ◽

Traffic Simulation ◽

Learning Approach ◽

Light Control ◽

Traffic Light ◽

Traffic Light Control ◽

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A Case Study in Hybrid Multi-threading and Hierarchical Reinforcement Learning Approach for Cooperative Multi-agent Systems

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) ◽

10.1109/micai.2015.20 ◽

2015 ◽

Author(s):

Hiram Ponce ◽

Ricardo Padilla ◽

Alan Davalos ◽

Alvaro Herrasti ◽

Cynthia Pichardo ◽

...

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

Multi Agent Systems ◽

Agent Systems ◽

Hierarchical Reinforcement Learning ◽

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A Decentralized Multi-agent Reinforcement Learning Approach for Resource Sharing and Allocation in Renewable Energy Powered Wireless Networks

Communications in Computer and Information Science - Artificial Intelligence and Security ◽

10.1007/978-981-15-8101-4_57 ◽

2020 ◽

pp. 640-651

Author(s):

Yu Gong ◽

Yifei Wei ◽

Qiao Li ◽

Lianping Zhang ◽

Xiaojun Wang

Keyword(s):

Wireless Networks ◽

Renewable Energy ◽

Reinforcement Learning ◽

Resource Sharing ◽

Learning Approach ◽

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Packet Routing Against Network Congestion: A Deep Multi-agent Reinforcement Learning Approach

2020 International Conference on Computing, Networking and Communications (ICNC) ◽

10.1109/icnc47757.2020.9049759 ◽

2020 ◽

Author(s):

Ruijin Ding ◽

Yuwen Yang ◽

Jun Liu ◽

Hongyan Li ◽

Feifei Gao

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

Packet Routing ◽

Network Congestion ◽

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Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) ◽

10.1109/coase.2019.8843223 ◽

2019 ◽

Author(s):

Sayyed Jaffar Ali Raza ◽

Mingjie Lin

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

Multi Agent Systems ◽

Agent Systems ◽

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Autonomous Separation Assurance in An High-Density En Route Sector: A Deep Multi-Agent Reinforcement Learning Approach

2019 IEEE Intelligent Transportation Systems Conference (ITSC) ◽

10.1109/itsc.2019.8917217 ◽

2019 ◽

Author(s):

Marc Brittain ◽

Peng Wei

Keyword(s):

Reinforcement Learning ◽

High Density ◽

Learning Approach ◽

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Autonomous Tracking Using a Swarm of UAVs: A Constrained Multi-Agent Reinforcement Learning Approach

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2020.3023733 ◽

2020 ◽

Vol 69 (11) ◽

pp. 13702-13717

Author(s):

Yu-Jia Chen ◽

Deng-Kai Chang ◽

Cheng Zhang

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

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Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2020.102861 ◽

2020 ◽

Vol 121 ◽

pp. 102861

Author(s):

Ke Zhang ◽

Fang He ◽

Zhengchao Zhang ◽

Xi Lin ◽

Meng Li

Keyword(s):

Reinforcement Learning ◽

Vehicle Routing ◽

Time Windows ◽

Learning Approach ◽

Vehicle Routing Problems ◽

Routing Problems ◽

Soft Time Windows ◽

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Urban Traffic Control in Software Defined Internet of Things via a Multi-Agent Deep Reinforcement Learning Approach

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2020.3023788 ◽

2020 ◽

pp. 1-13

Author(s):

Jiachen Yang ◽

Jipeng Zhang ◽

Huihui Wang

Keyword(s):

Reinforcement Learning ◽

Internet Of Things ◽

Traffic Control ◽

Urban Traffic ◽

Learning Approach ◽

Multi Agent ◽

Urban Traffic Control

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A multi-agent reinforcement learning approach to dynamic service composition

Information Sciences ◽

10.1016/j.ins.2016.05.002 ◽

2016 ◽

Vol 363 ◽

pp. 96-119 ◽

Author(s):

Hongbing Wang ◽

Xiaojun Wang ◽

Xingguo Hu ◽

Xingzhi Zhang ◽

Mingzhu Gu

Keyword(s):

Reinforcement Learning ◽

Service Composition ◽

Learning Approach ◽

Dynamic Service Composition ◽

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