June 5, 2024, 4:44 a.m. | Binghui Peng, Aviad Rubinstein

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02357v1 Announce Type: cross
Abstract: We study the iteration complexity of decentralized learning of approximate correlated equilibria in incomplete information games.
On the negative side, we prove that in $\mathit{extensive}$-$\mathit{form}$ $\mathit{games}$, assuming $\mathsf{PPAD} \not\subset \mathsf{TIME}(n^{\mathsf{polylog}(n)})$, any polynomial-time learning algorithms must take at least $2^{\log_2^{1-o(1)}(|\mathcal{I}|)}$ iterations to converge to the set of $\epsilon$-approximate correlated equilibrium, where $|\mathcal{I}|$ is the number of nodes in the game and $\epsilon > 0$ is an absolute constant. This nearly matches, up to the $o(1)$ term, …

abstract algorithms arxiv complexity converge cs.ai cs.ds cs.gt cs.lg decentralized equilibria equilibrium form games information iteration least negative polynomial prove study type

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