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And smooth differentiable function for all

We now develop in detail one class of learning methods for function approximation in

value prediction, those based on gradient descent. Gradient-descent methods are among

transpose), and is a smooth differentiable function of for all . For now,

let us assume that on each step, t, we observe a new example . These

states, or even all the examples, exactly correct. In addition, we must generalize to all the

other states that have not appeared in examples.

most reduce the error on that example:

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and smooth differentiable function for all
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