Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a decision-making algorithm that builds a search tree by simulating possible future outcomes, balancing exploration and exploitation to find optimal actions. Current research focuses on enhancing MCTS's efficiency and applicability in diverse domains, including quantum computing, mathematical reasoning, and autonomous agent control, often integrating it with large language models and reinforcement learning. These advancements are significantly impacting fields like AI planning, game playing, and robotics by enabling more efficient and effective decision-making in complex, uncertain environments.