Sequential Decision

Sequential decision-making focuses on optimizing a series of choices over time, considering the impact of each decision on future outcomes. Current research emphasizes efficient algorithms and model architectures, such as reinforcement learning (including Deep Q-Networks and Decision Transformers), Monte Carlo Tree Search, and various transformer-based approaches, to tackle challenges like partial observability, non-stationarity, and fairness in decision sequences. These advancements are improving the performance of autonomous systems in diverse applications, from autonomous driving and resource management to personalized financial advice and healthcare. The field is also actively exploring methods for enhancing interpretability and addressing the limitations of existing models in complex, real-world scenarios.