Sampling Based Approach

Sampling-based approaches are computational techniques that address challenges in high-dimensional spaces or computationally expensive problems by strategically selecting a subset of data points or actions for analysis. Current research focuses on improving the efficiency and accuracy of these methods, particularly within reinforcement learning, motion planning (using algorithms like RRT and its variants), and clustering, often incorporating techniques like importance sampling or heuristic-based sampling to reduce variance and bias. These advancements are significant because they enable the application of complex algorithms to large datasets and intricate problems in diverse fields, including robotics, machine learning, and healthcare, where exhaustive search is infeasible.