Decision Time Planning

Decision-time planning focuses on improving decision-making in dynamic environments by incorporating planning directly into the execution phase, rather than relying solely on pre-computed policies. Current research emphasizes developing efficient algorithms, such as those based on mirror descent and deep reinforcement learning, to handle imperfect information and high-dimensional state spaces, particularly in games and robotics. This approach shows promise for enhancing the adaptability and robustness of autonomous systems in complex, real-world scenarios, as demonstrated by successful applications in areas like robotic manipulation and autonomous navigation. The development of more efficient and generalizable decision-time planning methods is a significant area of ongoing research.