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- W2462621446 abstract "We consider the problem of sequential decision making in non-stationary environments. In order to avoid solutions that are too conservative, we capture the degree of non-stationarity in real-world problems through different mathematical models for the environment. In the first model, we add to the environment a state that follows Markovian dynamics subject to limited levels of non-stationary uncertainty. In the second model, we add non-stationary constraints to the environment. In the third model, we limit the frequency of non-stationary changes. In each of these models, we provide efficient learning algorithms and prove corresponding performance guarantees that depend critically on the degree of non-stationarity." @default.
- W2462621446 created "2016-07-22" @default.
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- W2462621446 date "2010-01-01" @default.
- W2462621446 modified "2023-09-24" @default.
- W2462621446 title "Sequential decision making in non-stationary environments" @default.
- W2462621446 hasPublicationYear "2010" @default.
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