Optimized control for human-multi-robot collaborative manipulation via multi-player Q-learning

DOI10.1016/j.jfranklin.2021.03.017zbMath1467.93224OpenAlexW3163921708MaRDI QIDQ2041406

Xing Liu, Panfeng Huang, Shuzhi Sam Ge

Publication date: 19 July 2021

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2021.03.017

zbMATH Keywords

human-multi-robot collaboration controlmulti-player non-zero sum gameoptimized interaction control

Mathematics Subject Classification ID

Applications of game theory (91A80) Automated systems (robots, etc.) in control theory (93C85) (n)-person games, (n>2) (91A06)

Related Items (1)

Model-free finite-horizon optimal tracking control of discrete-time linear systems

Cites Work

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• Output-feedback adaptive optimal control of interconnected systems based on robust adaptive dynamic programming
• Multi-player non-zero-sum games: online adaptive learning solution of coupled Hamilton-Jacobi equations
• Non-zero sum Nash Q-learning for unknown deterministic continuous-time linear systems
• Adaptive optimal control for continuous-time linear systems based on policy iteration
• Adaptive optimal tracking controls of unknown multi-input systems based on nonzero-sum game theory
• Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming
• Impedance adaptation for optimal robot–environment interaction

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