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methods of encoding
 1.
 Linear Function
We choose a weight function,
,
for which the
value function is
.
Naturally, we cannot calculate every function
this way, but in many cases it gives good results. Another
advantage is the simplicity of calculating the derivative
.
The derivative is the encoding of the state, which is very
convenient computationwise.
 2.
 Neural Networks (figure )
Figure:
A Neural Network

Figure:
Calculating The Value Of A Gate

Calculation of a gate (see figure ):
First, we
compute
.
Then give
as an
argument to a non linear function.
 1.
 perceptron :
The problem: it isn't a continuous function.
 2.
 sigmoid function: a continuous perceptron approximation,
Note that,
,
and when
,
,
and when
,
.
It is
possible to connect a large number of such gates, each gate has
its own weight vector w_{i}. There are simple algorithms for
computing the derivative by using the chain rule.
Basically, we are left with a learning problem: finding F(r,s)
that corresponds to V^{*}. We are interested in two things:
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Yishay Mansour
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