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• W2913966743 abstract "We use a passivity-based methodology for the analysis and design of reinforcement learning in multi-agent games. We consider an exponentially-discounted reinforcement learning scheme, and show that convergence can be guaranteed for the class of games characterized by the monotonicity property of their (negative) payoff. We further exploit passivity properties to propose a class of higher-order schemes that preserve convergence properties." @default.
• W2913966743 created "2019-02-21" @default.
• W2913966743 creator A5025140886 @default.
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• W2913966743 date "2018-12-01" @default.
• W2913966743 modified "2023-09-24" @default.
• W2913966743 title "On Passivity and Reinforcement Learning in Finite Games" @default.
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• W2913966743 doi "https://doi.org/10.1109/cdc.2018.8619157" @default.
• W2913966743 hasPublicationYear "2018" @default.
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