Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Sim, Ryann"'
As quantum processors advance, the emergence of large-scale decentralized systems involving interacting quantum-enabled agents is on the horizon. Recent research efforts have explored quantum versions of Nash and correlated equilibria as solution con
Externí odkaz:
http://arxiv.org/abs/2310.08473
Gamification is an emerging trend in the field of machine learning that presents a novel approach to solving optimization problems by transforming them into game-like scenarios. This paradigm shift allows for the development of robust, easily impleme
Externí odkaz:
http://arxiv.org/abs/2302.04789
In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Similarly to the seminal work of Nemirovski and the recent approach of Pilio
Externí odkaz:
http://arxiv.org/abs/2301.03931
Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of learning algorithms for classical games into the quantum realm. In this p
Externí odkaz:
http://arxiv.org/abs/2211.01681
The study of learning in games has thus far focused primarily on normal form games. In contrast, our understanding of learning in extensive form games (EFGs) and particularly in EFGs with many agents lags far behind, despite them being closer in natu
Externí odkaz:
http://arxiv.org/abs/2207.08426
In this paper, we provide a novel and simple algorithm, Clairvoyant Multiplicative Weights Updates (CMWU) for regret minimization in general games. CMWU effectively corresponds to the standard MWU algorithm but where all agents, when updating their m
Externí odkaz:
http://arxiv.org/abs/2111.14737
A seminal result in game theory is von Neumann's minmax theorem, which states that zero-sum games admit an essentially unique equilibrium solution. Classical learning results build on this theorem to show that online no-regret dynamics converge to an
Externí odkaz:
http://arxiv.org/abs/2111.03377
The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game. In this paper, we move away from the art
Externí odkaz:
http://arxiv.org/abs/2012.08382