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• W2742389002 abstract "This paper examines the problem of multi-agent learning in $$N$$-person non-cooperative games. For concreteness, we focus on the so-called “hedge” variant of the (EW) algorithm, one of the most widely studied algorithmic schemes for regret minimization in online learning. In this multi-agent context, we show that (a) dominated strategies become extinct (a.s.); and (b) in generic games, pure Nash equilibria are attracting with high probability, even in the presence of uncertainty and noise of arbitrarily high variance. Moreover, if the algorithm’s step-size does not decay too fast, we show that these properties occur at a quasi-exponential rate – that is, much faster than the algorithm’s $${{mathrm{mathcal O}}}(1/sqrt{T})$$ worst-case regret guarantee would suggest." @default.
• W2742389002 created "2017-08-17" @default.
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• W2742389002 date "2017-01-01" @default.
• W2742389002 modified "2023-10-16" @default.
• W2742389002 title "Hedging Under Uncertainty: Regret Minimization Meets Exponentially Fast Convergence" @default.
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• W2742389002 doi "https://doi.org/10.1007/978-3-319-66700-3_20" @default.
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