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- W3013920237 abstract "In this paper, we focus on the role played by information in designing efficient learning rules for agents involved in games over networks. We show how the need to cope with partial information in networks, or alternatively, the preference to leverage side information can lead to new distributed game-theoretic learning algorithms.Observing all others’ decisions as considered in the classical approach in fictitious-type or projected-gradient-type algorithms can be impractical in distributed networks. We show how in such settings, the lack of full information on the opponents’ decisions can be compensated by local information exchange with neighbours. On the other hand, reinforcement learning algorithms only require that each player knows its played action and realized payoff. Often in networks agents have more information than just this minimal information required by classical reinforcement learning. We describe a general framework enabling players to exploit some additional side information when available." @default.
- W3013920237 created "2020-04-03" @default.
- W3013920237 creator A5038039270 @default.
- W3013920237 date "2019-11-01" @default.
- W3013920237 modified "2023-09-27" @default.
- W3013920237 title "On the Role of Information in Learning in Games over Networks" @default.
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- W3013920237 doi "https://doi.org/10.1109/ieeeconf44664.2019.9048800" @default.
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