Sequential Decision Making Problem

Sequential decision-making problems involve choosing a series of actions to optimize a long-term outcome, encompassing diverse applications from robotics and healthcare to finance and game playing. Current research focuses on improving the efficiency and robustness of algorithms like reinforcement learning (including Q-learning and its variants), Thompson sampling, and Monte Carlo Tree Search, often incorporating model architectures such as transformers and graph neural networks to handle complex state spaces and non-stationary environments. These advancements aim to address challenges like risk sensitivity, fairness, generalization to unseen data (out-of-distribution detection), and interpretability of learned policies, ultimately leading to more reliable and effective autonomous systems in various domains.