  We present a learning based framework for mesh quality improvement on
unstructured triangular and quadrilateral meshes. Our model learns to improve
mesh quality according to a prescribed objective function purely via self-play
reinforcement learning with no prior heuristics. The actions performed on the
mesh are standard local and global element operations. The goal is to minimize
the deviation of the node degrees from their ideal values, which in the case of
interior vertices leads to a minimization of irregular nodes.
