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- W3194476246 abstract "We show that Optimistic Hedge -- a common variant of multiplicative-weights-updates with recency bias -- attains ${rm poly}(log T)$ regret in multi-player general-sum games. In particular, when every player of the game uses Optimistic Hedge to iteratively update her strategy in response to the history of play so far, then after $T$ rounds of interaction, each player experiences total regret that is ${rm poly}(log T)$. Our bound improves, exponentially, the $O({T}^{1/2})$ regret attainable by standard no-regret learners in games, the $O(T^{1/4})$ regret attainable by no-regret learners with recency bias (Syrgkanis et al., 2015), and the ${O}(T^{1/6})$ bound that was recently shown for Optimistic Hedge in the special case of two-player games (Chen & Pen, 2020). A corollary of our bound is that Optimistic Hedge converges to coarse correlated equilibrium in general games at a rate of $tilde{O}left(frac 1Tright)$." @default.
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- W3194476246 date "2021-08-16" @default.
- W3194476246 modified "2023-09-27" @default.
- W3194476246 title "Near-Optimal No-Regret Learning in General Games" @default.
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