Computational Lower Bounds for Efficiently Finding Approximate Correlated Equilibria in Normal-Form Games

This research paper provides evidence that efficiently finding approximate correlated equilibria (CE) in normal-form games, even with relaxed sparsity constraints, is computationally hard, suggesting current no-regret learning algorithms are near-optimal.