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The original was posted on /r/getmotivated by /u/NextGen-Trading on 2023-12-19 16:50:39+00:00.
I first heard of “Reinforcement Learning” when I was a junior at Cornell University in a course called “Foundations of Artificial Intelligence”. This course was an eye-opener, introducing me to various subfields within AI such as neural networks and genetic optimization. But what really caught my attention was Reinforcement Learning.
For those not steeped in technical jargon, Reinforcement Learning (RL) is a subfield of Machine Learning (ML). Unlike most ML which relies on supervised learning — training algorithms with labeled examples — Reinforcement Learning takes a different approach. It uses a system of rewards and punishments to ‘teach’ the computer, much like how you might train a dog.
For example, imagine training a new puppy you got from the shelter. When the puppy sits on command, you give him a treat and lots of “good boys”. However, when the puppy pees on the carpet, you scold him, so he learns to not do it again. RL applies this same principle to computers, enabling them to learn from their actions.
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