What Is Reinforcement Learning (RL)?

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Reinforcement learning is like a video game in which you're the player, and the machine is the game. The device has to learn how to play, but it doesn't know what it's supposed to do—it just knows what happens when it does something. You have to tell it what to do by giving it rewards or punishments for its actions. You can think about this in a video game that lets you play as a cat trying to get through an obstacle course. The cat doesn't know anything about the maze—it just knows that if it moves forward, there's a reward (a piece of food), but if it moves backward, there's a punishment (pain). It tries different combinations of moves until it figures out how to successfully get through the maze. Reinforcement learning is an algorithm that lets you teach machines to learn from their environment. It's the technology behind self-driving cars and how we'll introduce our robot overlords to be excellent. Reinforcement learning is a way for your computer to learn from its environment. The machine is rewarded for performing specific tasks correctly and penalized for other jobs incorrectly. The engine then uses this information to maximize its compensation and penalty—all without human intervention! This system helps machines learn independently without being told explicitly what they should do by a human. It's like teaching your dog not to pee on the carpet by rewarding him when he pees outside but punishing him when he pees inside. The agent, over time, makes decisions to maximize its reward and minimize its penalty using dynamic programming. The advantage of this approach to artificial intelligence is that it allows an AI program to learn without a programmer spelling out how an agent should perform the task. It's like raising a baby. You don't say, "now you need to crawl" or "now you need to walk." You just let the baby do what it needs to and provide plenty of love and support along the way!

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