Reinforcement Learning and Control as Probabilistic Inference: Tutorial andReview

Reinforcement Learning and Probability: Teaching Machines to Decide

Think of teaching a robot or app how to make good choices by trial and error.
That idea, called learning, can be seen another way — as guessing with probability.
When we treat decisions like smart guesses we can use powerful tools from stats, and that makes building programs easier and more flexible, even when they only see part of the world.
In simple, predictable situations the guesswork lines up exactly with the rules for making choices, but when the world is noisy it becomes an useful approximation.
This bridge between decision making and probability helps designers reuse tools, make models that cope with uncertainty, and handle tricky settings like partial vision or changing rules.
The idea has already shaped new ways to build controllers and learning systems, and it points toward richer methods for future projects.
It’s a fresh way to think about how machines decide, and why sometimes choosing is really just smart guessing about what might happen.

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Reinforcement Learning and Control as Probabilistic Inference: Tutorial andReview

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