Sequential Decision Making

Sequential decision-making (SDM) focuses on optimizing a series of actions over time to achieve a desired outcome in uncertain environments. Current research emphasizes developing efficient algorithms, often based on reinforcement learning (RL) or Bayesian methods, to handle complex scenarios with multiple objectives, delayed feedback, and high-dimensional data, incorporating techniques like Thompson sampling, Monte Carlo Tree Search, and various neural network architectures. SDM's significance lies in its broad applicability across diverse fields, from personalized medicine and financial modeling to robotics and resource management, driving advancements in both theoretical understanding and practical problem-solving.