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Monte Carlo Tree Search (MCTS) is a statistical heuristic algorithm that builds decision trees through selection, expansion, simulation, and backpropagation, enabling breakthroughs like AlphaGo’s victory and finding applications in robotics, autonomous driving, finance, and bioinformatics.
The article reviews AlphaGo’s core deep‑learning and reinforcement‑learning technologies, explains reinforcement‑learning fundamentals, and explores their wide‑range applications from epidemic forecasting and medical decision support to smart logistics, vehicle dispatch, and resource allocation, while highlighting challenges and future opportunities.
The talk surveyed game‑AI evolution—from rule‑based systems to AlphaGo‑style reinforcement learning—highlighted industry and academic methods, detailed challenges of applying deep‑learning techniques to MOBA titles like Honor Kings, and proposed a hierarchical, multimodal framework with analysis and execution modules supported by robust simulation environments.