  This paper proposes, implements, and evaluates a reinforcement learning
(RL)-based computational framework for automatic mesh generation. Mesh
generation plays a fundamental role in numerical simulations in the area of
computer aided design and engineering (CAD/E). It is identified as one of the
critical issues in the NASA CFD Vision 2030 Study. Existing mesh generation
methods suffer from high computational complexity, low mesh quality in complex
geometries, and speed limitations. These methods and tools, including
commercial software packages, are typically semiautomatic and they need inputs
or help from human experts. By formulating the mesh generation as a Markov
decision process (MDP) problem, we are able to use a state-of-the-art
reinforcement learning (RL) algorithm called "soft actor-critic" to
automatically learn from trials the policy of actions for mesh generation. The
implementation of this RL algorithm for mesh generation allows us to build a
fully automatic mesh generation system without human intervention and any extra
clean-up operations, which fills the gap in the existing mesh generation tools.
In the experiments to compare with two representative commercial software
packages, our system demonstrates promising performance with respect to
scalability, generalizability, and effectiveness.
