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cs.GT2025
Meta-Learning in Self-Play Regret Minimization
David Sychrovský, Martin Schmid, Michal Šustr +1
Regret minimization is a general approach to online optimization which plays a crucial role in many algorithms for approximating Nash equilibria in two-player zero-sum games. The l…
cs.GT2025
Approximating Nash Equilibria in General-Sum Games via Meta-Learning
David Sychrovský, Christopher Solinas, Revan MacQueen +4
Nash equilibrium is perhaps the best-known solution concept in game theory. Such a solution assigns a strategy to each player which offers no incentive to unilaterally deviate. Whi…