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- W1915057100 abstract "We consider a general reinforcement learning problem and show that carefully combining the Bayesian optimal policy and an exploring policy leads to minimax sample-complexity bounds in a very general class of (history-based) environments. We also prove lower bounds and show that the new algorithm displays adaptive behaviour when the environment is easier than worst-case." @default.
- W1915057100 created "2016-06-24" @default.
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- W1915057100 date "2014-01-01" @default.
- W1915057100 modified "2023-09-26" @default.
- W1915057100 title "Bayesian Reinforcement Learning with Exploration" @default.
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- W1915057100 doi "https://doi.org/10.1007/978-3-319-11662-4_13" @default.
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