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Reinforcement Learning (RL)
Reinforcement Learning is an area of machine learning where an 'agent' learns to make decisions by interacting with an environment. It receives rewards for desirable actions and penalties for undesirable ones, aiming to maximize its cumulative reward over time. This trial-and-error process allows the agent to discover optimal strategies without explicit programming.
In plain terms
It's like teaching a dog tricks with treats and scolding, rather than giving it a detailed instruction manual.
Why it matters
RL enables AI to tackle complex problems requiring sequential decision-making, from game playing to robotics and autonomous systems.
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