# No-Regret Learning in Bayesian Games

arXiv: https://arxiv.org/abs/1507.00418 · HTML: https://mlsanity.com/paper/1507.00418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2015.

## Built on by (most cited first, 1 of 1)

- [Online Stackelberg Optimization via Nonlinear Control](https://mlsanity.com/paper/2406.18805/) (2024)

## Abstract (arXiv)

Recent price-of-anarchy analyses of games of complete information suggest that coarse correlated equilibria, which characterize outcomes resulting from no-regret learning dynamics, have near-optimal welfare. This work provides two main technical results that lift this conclusion to games of incomplete information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian games follows directly from the smoothness-based proof of near-optimal welfare in the same game when the private information is public. Second, no-regret learning dynamics converge to Bayesian coarse correlated equilibrium in these incomplete information games. These results are enabled by interpretation of a Bayesian game as a stochastic game of complete information.