ARS (Augmented Random Search)

Augmented Random Search (ARS) is an optimization technique used in machine learning to improve the performance of algorithms. It works by generating a variety of random solutions and evaluating their effectiveness in solving a problem. Instead of solely relying on the best solutions, ARS enhances those that perform well and explores new variations, allowing it to learn from both successes and failures. This approach helps find better strategies more efficiently, making it particularly useful for training artificial intelligence, such as teaching a robot how to perform tasks or optimize complex processes without requiring extensive prior knowledge.