  We present a method to generate directed acyclic graphs (DAGs) using deep
reinforcement learning, specifically deep Q-learning. Generating graphs with
specified structures is an important and challenging task in various
application fields, however most current graph generation methods produce
graphs with undirected edges. We demonstrate that this method is capable of
generating DAGs with topology and node types satisfying specified criteria in
highly sparse reward environments.
