Learning in Matrix Games can be Arbitrarily Complex

Understand

A growing number of machine learning architectures, such as Generative Adversarial Networks, rely on the design of games which implement a desired functionality via a Nash equilibrium.

• In practice these games have an implicit complexity (e.g.
• from underlying datasets and the deep networks used) that makes directly computing a Nash equilibrium impractical or impossible.
• For this reason, numerous learning algorithms have been developed with the goal of iteratively converging to a Nash equilibrium.