  The quality of mesh generation has long been considered a vital aspect in
providing engineers with reliable simulation results throughout the history of
the Finite Element Method (FEM). The element extraction method, which is
currently the most robust method, is used in business software. However, in
order to speed up extraction, the approach is done by finding the next element
that optimizes a target function, which can result in local mesh of bad quality
after many time steps. We provide TreeMesh, a method that uses this method in
conjunction with reinforcement learning (also possible with supervised
learning) and a novel Monte-Carlo tree search (MCTS) (Coulom(2006), Kocsis and
Szepesv\'ari(2006), Browne et~al.(2012)). The algorithm is based on a
previously proposed approach (Pan et~al.(2021)). After making many improvements
on DRL (algorithm, state-action-reward setting) and adding a MCTS, it
outperforms the former work on the same boundary. Furthermore, using tree
search, our program reveals much preponderance on seed-density-changing
boundaries, which is common on thin-film materials.
