Sampling Tree

Sampling trees are hierarchical data structures used in various applications to efficiently explore and sample from complex spaces, particularly in decision-making and path planning problems. Current research focuses on optimizing sampling strategies within these trees, including the development of goal-guided and dynamic sampling policies to improve efficiency and accuracy, often employing algorithms like Monte Carlo Tree Search (MCTS) and variations of Rapidly-exploring Random Trees (RRT). These advancements have significant implications for diverse fields, such as autonomous navigation (e.g., for aerial and underwater vehicles) and reinforcement learning, enabling more efficient and effective solutions to complex problems.