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Efficiently computing normal-form correlated equilibria in Markov games is now possible, bridging a long-standing gap since Papadimitriou and Roughgarden's seminal work.
Access to simulators can yield classical learning guarantees even in the presence of complex data dependencies, fundamentally shifting our understanding of learning theory.
