Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Near-Optimal No-Regret Learning in General Games   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 $\tilde{O}\left(\frac 1T\right)$.

7
The categories of this paper are: hep-th
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph