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ELI5: reinforcement learning
// explanation
What is reinforcement learning?
Reinforcement learning is like teaching a robot or computer to do something by giving it rewards when it does well and no rewards when it does poorly [3][4]. It's similar to how you learn to play a video game—you try different moves, and when you succeed, you remember what worked [2].
Why does it work?
The computer learns through trial and error, just like animals and humans do [2]. Every time it tries something and gets rewarded (like points in a game), it learns that action was good and tries to do it again [5].
What does it do?
The computer makes decisions to get the best rewards possible, kind of like how you'd learn the fastest way to solve a puzzle by trying different approaches [4]. It keeps practicing until it figures out the best strategy.
How is it used?
Reinforcement learning helps teach computers to play games, drive cars, and make smart choices in situations where they don't have instructions—they just learn what works [1][3].
// sources
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment ...
Of all the forms of machine learning, reinforcement learn- ing is the closest to the kind of learning that humans and other animals do, and many of the core ...
Reinforcement learning (RL) is a type of machine learning process in which autonomous agents learn to make decisions by interacting with their environment. An ...
Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results.
Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents can learn to make decisions through trial and error to maximize ...
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