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Learning Nash Equilibria in Rank-1 Games

ICLR 2024 · Learning theory and algorithms › Submodular and game algorithms · approach: Equilibrium solvers for adversarial games

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Studies learning Nash equilibria in rank-1 games, a class of (non-monotone, non-potential) games whose payoff structure has rank-1 form, and proposes an efficient learning approach for them.

Problem

Learning Nash equilibria efficiently in games beyond those satisfying the (weak) Minty property or being potential games, relevant to GAN training and multi-agent RL.

Approach

Targets rank-1 games, a structured class of games that goes beyond Minty-property and potential games, and gives an approach for efficiently learning their Nash equilibria. Details beyond the abstract are not available.

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