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No-Regret Learning in Games is Turing Complete
Games are natural models for multi-agent machine learning settings, such as generative adversarial networks (GANs).
Graphical Economies with Resale
Kakade, Kearns, and Ortiz (KKO) introduce a graph-theoretic generalization of the classic Arrow--Debreu (AD) exchange economy.
Learning in Matrix Games can be Arbitrarily Complex
In this paper we show that, in a strong sense, this dynamic complexity is inherent to games.
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