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/Learning Paradigms
/Reinforcement Learning
/Q-Learning

Q-Learning

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Q-Learning

Q-learning is a trial-and-error approach where machines learn the value of actions in different states by maintaining a Q-table of state-action pairs with expected rewards.

Example: Teaching a dog to navigate a house. At first, its moves are random; when it finds treats, it remembers which moves worked. Over time, its Q-table builds an internal map, allowing it to choose the best actions. Example: A robot in a maze receiving +10 points for reaching the exit and -5 for hitting walls.