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Reinforcement Learning (RL) Framework
Reinforcement Learning (RL) is a paradigm where an 'agent' learns to make decisions by interacting with an 'environment' to achieve a goal. The agent receives 'rewards' for desirable actions and 'penalties' for undesirable ones, learning through trial and error to maximize its cumulative reward over time. This approach doesn't require pre-labeled data, instead discovering optimal behavior through exploration.
In plain terms
It's like teaching a dog tricks using treats as rewards for correct actions.
Why it matters
RL is fundamental for creating AI systems that can learn to operate autonomously in dynamic environments, from robotics to game playing.
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