Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a method used to make decisions in complex situations, like games or planning problems. It builds a tree of possible future actions and outcomes by simulating random plays. MCTS consists of four steps: selection, where it explores the best-known paths; expansion, where new actions are added; simulation, where random outcomes are played out; and backpropagation, where results update the knowledge of previous actions. This process helps identify the most promising moves based on statistical outcomes, ultimately guiding choices that maximize success in uncertain environments.