Sequential Decision Making Task

Sequential decision-making tasks involve choosing a series of actions to optimize long-term outcomes in dynamic environments. Current research focuses on improving the efficiency and generalizability of decision-making agents, employing techniques like reinforcement learning (RL), transformer architectures (e.g., Decision Transformer, Decision Mamba), and large language models (LLMs) to handle complex scenarios and learn from limited data. These advancements are significant for various applications, including robotics, healthcare, and personalized systems, by enabling more robust, adaptable, and explainable AI agents. Furthermore, research emphasizes improving the interpretability of these agents and developing methods for efficient evaluation of long-term policy effects.