7) REINFORCEMENT LEARNING

There are three models for machine learning: reinforcement learning, unsupervised learning, and supervised learning. In reinforcement learning, the computer program gains knowledge by directly interacting with its surroundings.

The observations that the ML system perceives can have a value assigned to them by the environment using a reward/punishment mechanism. Similar to positive reinforcement training for animals, the system's ultimate goal will be to maximize reward or value.

This has numerous applications in AI for video games and board games. However, reinforcement ML might not be the ideal choice when safety is a crucial component of the application. The algorithm may purposefully make risky decisions as it learns since it draws conclusions from random actions. Leaving this could put users in danger. netizenstechnologies.com