3 Method of calculation of ergodic and differen-
ce matrices
We consider dependence for total discounted rewards given by formula (10) again.
ν∞ (β) = (I - βP)-1 ∙ q. (11)
It is not difficult to notice, that
(I - βP) = det (i — βp) (I - βp) ad, (12)
where (I - βP) ad is an algebraically complement of matrix (I - βP). Next
we can write
(I - βP) ad = [Dji (β)], i,j = 1,N
(13)
where Dji (β) = (—1)j+i ∙ Mji (β), and Mji (β) is a minor of matrix (I — βP)T,
hence
(I-βP)-1
[Dji (β)]
det (I — βP)
(14)
Theorem:
Let determinant of matrix (I — βP) have real and singular roots, then for each
stochastic matrix P and factor β < 1 exist such αk 6= 0, k = 1, 2, . . . , N that true
is the following formula:
(I — βP )-1
JDiiL + [D2i] +...+JDNL
(1 — αιβ) (1 — α2β) (1 — αNβ),
where
(15)
det (I — βP) = (1 — α1β) (1 — α2β) . . . (1 — αNβ) . . . , (16)
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