Exploration augmentation

Exploration augmentation is a technique used in machine learning where algorithms intentionally try new or less certain actions to gather more information about their environment. This approach helps the system discover better strategies by balancing the act of exploiting what it already knows with exploring new possibilities. By augmenting the learning process with exploration, the algorithm becomes better at understanding complex situations and improves its decision-making over time. Essentially, it encourages a machine to venture into unknown options to enhance learning and optimize outcomes more effectively.