Augmented Random Search

Augmented Random Search (ARS) is a simple, efficient method used in machine learning to teach computers to make decisions, especially in complex tasks. It works by starting with a basic strategy and then quickly trying small random changes to see if they improve performance. If a change helps, it’s kept; if not, it’s discarded. Over time, these small adjustments help the computer develop a more effective way to perform the task. ARS is valued for being straightforward, requiring less computation, and being effective in training systems like robots or game-playing agents.