Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot collaborative disassembly: A case of retired electric vehicle battery Article Swipe

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Jiaxu Gao , Guoxian Wang , Jinhua Xiao , Pai Zheng , Eujin Pei ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1016/j.rcim.2024.102775

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Reinforcement Learning

Artificial Intelligence

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Reinforcement learning Battery (electricity) Markov decision process Task (project management) Process (computing) Robot Observability Sequence (biology) Powertrain Computer science Engineering Artificial intelligence Simulation Markov process Systems engineering Power (physics) Genetics Biology Physics Quantum mechanics Thermodynamics Operating system Torque Statistics Applied mathematics Mathematics

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.rcim.2024.102775
OA Status: green
Cited By: 23
References: 53
Related Works: 10
OpenAlex ID: https://openalex.org/W4394982298

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DOI: https://doi.org/10.1016/j.rcim.2024.102775

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Title: Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot collaborative disassembly: A case of retired electric vehicle battery

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Language: en

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Publication year: 2024

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Publication date: 2024-04-21

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Authors: Jiaxu Gao, Guoxian Wang, Jinhua Xiao, Pai Zheng, Eujin Pei

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Landing page: https://doi.org/10.1016/j.rcim.2024.102775

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Concepts: Reinforcement learning, Battery (electricity), Markov decision process, Task (project management), Process (computing), Robot, Observability, Sequence (biology), Powertrain, Computer science, Engineering, Artificial intelligence, Simulation, Markov process, Systems engineering, Power (physics), Genetics, Biology, Physics, Quantum mechanics, Thermodynamics, Operating system, Torque, Statistics, Applied mathematics, Mathematics

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Cited by: 23

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