title: Smooth markets: A basic mechanism for organizing gradient-based learners
abstract: With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes some GANs, adversarial training, and other recent algorithms. We show that SM-games are amenable to analysis and optimization using first-order methods.
authors: David Balduzzi Wojciech M. Czarnecki Tom Anthony Ian Gemp Edward Hughes Joel Leibo Georgios Piliouras Thore Graepel
hIndex: Thore Graepel 52