Reinforcement Learning

Reinforcement learning is a sub-field of machine learning that involves intelligent agents interacting with their environment to maximize cumulative rewards. Unlike supervised learning, reinforcement learning does not rely on labeled datasets and instead uses rewards and punishments to correct sub-optimal actions. The objective of reinforcement learning is to build a model that enhances the agent's total cumulative reward by mapping actions to states. A classic example of reinforcement learning is the game "Pacman," where the agent aims to eat as much food as possible while avoiding ghosts.