  Finding optimal configurations in a geometric space is a key challenge in
many technological disciplines. Current approaches either rely heavily on human
domain expertise and are difficult to scale. In this paper we show it is
possible to solve configuration optimization problems for whole-page
recommendation using reinforcement learning. The proposed \textit{Tile
Networks} is a neural architecture that optimizes 2D geometric configurations
by arranging items on proper positions. Empirical results on real dataset
demonstrate its superior performance compared to traditional learning to rank
approaches and recent deep models.
