Reinforcement Learning (as a contrasting type)

Reinforcement learning is a type of machine learning where an agent learns to make decisions by trying actions and receiving feedback in the form of rewards or penalties. It’s similar to training a pet: the agent explores different options, and based on the outcomes, it learns which actions lead to the best results over time. Unlike some methods that rely on instructions, reinforcement learning focuses on discovering optimal strategies through experience, making it effective for complex tasks like game playing, robotics, or navigation, where the right actions are learned through trial and error.