  The Kyle model describes how an equilibrium of order sizes and security
prices naturally arises between a trader with insider information and the price
providing market maker as they interact through a series of auctions. Ever
since being introduced by Albert S. Kyle in 1985, the model has become
important in the study of market microstructure models with asymmetric
information. As it is well understood, it serves as an excellent opportunity to
study how modern deep learning technology can be used to replicate and better
understand equilibria that occur in certain market learning problems.
  We model the agents in Kyle's single period setting using deep neural
networks. The networks are trained by interacting following the rules and
objectives as defined by Kyle. We show how the right network architectures and
training methods lead to the agents' behaviour converging to the theoretical
equilibrium that is predicted by Kyle's model.
