Advances in generative modeling and adversarial learning have given rise to renewed interest in differentiable two-players games, with much of the attention falling on generative adversarial networks (GANs). Solving these games introduces distinct challenges compared to the standard minimization tasks that the machine learning (ML) community is used to. A symptom of this issue is ML and deep learning (DL) practitioners using optimization tools on game-theoretic problems. Recent work seeks to rectify this situation by bringing game theoretic tools into ML. At NeurIPS 2018 we held "Smooth games optimization in ML", a workshop with this scope and goal in mind.

In a previous story, I wrote about how a Game Theoretic approach was influencing developments in the Deep Learning field. In this story, I now write about DeepMind's latest foray into this exciting area. Yesterday, February 19th 2017), DeepMind presents their latest research on this subject titled "Understanding Agent Cooperation". The gist of the research is that, they employed Deep Reinforcement Learning networks in two game environments to study their behavior. The motivation is to study multi-agent systems to better understand and control these kinds of systems. In a previous story (see: "Five Capability Levels of Deep Learning", I laid out a road map as to how Deep Learning will evolve in even greater capabilities.