Per wiki, MCTS was combined with neural networks in 2016[1] and has been used in multiple board games like Chess, Shogi,[2] Checkers, Backgammon, Contract Bridge, Computer Go, Scrabble, and Clobber[3] as well as in turn-based-strategy video games (such as Total War: Rome II’s implementation in the high level campaign AI[4]). MCTS has also been used in self-driving cars, for example in Tesla’s Autopilot software.

Monte Carlo Tree Search is composed of the following 4 steps: Tree traversal, node expansion, rollout (random simulation) and backpropagation. The focus of MCTS is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space.