Reinforcing the Science Behind Reinforcement Learning

Reinforcing the Science Behind Reinforcement Learning

Dummies guide to Reinforcement learning, Q learning, Bellman Equation. You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.

You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.

You launched Chess and chose to play against the computer, and you lost!

But how did that happen? How can you lose against a machine that came into existence like 50 years ago?

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This is the magic of** Reinforcement learning.**

Reinforcement learning **lies under the umbrella of Machine Learning. **They aim at developing intelligent behavior in a complex dynamic environment. Nowadays since the range of AI is expanding enormously, we can easily locate their importance around us. From _Autonomous Driving, Recommender Search Engines, Computer games to Robot skills, _AI is playing a vital role.

Pavlov’s Conditioning

When we think about AI, we have a perception of thinking about the future, but our idea takes us back in the late 19th century, Ivan Pavlov, a Russian physiologist was studying the salivation effect in dogs. He was interested in knowing how much dogs salivate when they see food, but, while conducting the experiment, he noticed that dogs were even salivating before seeing any food. After his conclusions on that experiment, Pavlov would ring a bell before feeding them and as expected they again started salivating. The reason behind their behavior can be their ability to learn** because they had learned that after the bell, they’ll be fed**. Another thing to ponder is, the dog doesn’t salivate because the bell is ringing but because given past experiences he had learned that food will follow the bell.

deep-learning artificial-intelligence reinforcement-learning data-science machine-learning deep learning

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