Download Continuous-Time Markov Decision Processes: Theory and by Xianping Guo PDF

By Xianping Guo

Continuous-time Markov determination approaches (MDPs), sometimes called managed Markov chains, are used for modeling decision-making difficulties that come up in operations examine (for example, stock, production, and queueing systems), laptop technology, communications engineering, keep an eye on of populations (such as fisheries and epidemics), and administration technological know-how, between many different fields. This quantity presents a unified, systematic, self-contained presentation of contemporary advancements at the thought and purposes of continuous-time MDPs. The MDPs during this quantity comprise lots of the instances that come up in functions, simply because they enable unbounded transition and reward/cost charges. a lot of the fabric appears to be like for the 1st time in booklet form.

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Additional info for Continuous-Time Markov Decision Processes: Theory and Applications

Sample text

We also have the following lemma. 1 For all f ∈ F and t ≥ 0, the following assertions hold: (a) P (t, f )P ∗ (f ) = P ∗ (f )P (t, f ) = P ∗ (f )P ∗ (f ) = P ∗ (f ), and P ∗ (f )e = e. (b) Q(f )P ∗ (f ) = P ∗ (f )Q(f ) = 0, and V¯ (f ) = P ∗ (f )r(f ). (c) (P (t, f ) − P ∗ (f ))n = P (nt, f ) − P ∗ (f ) for all integers n ≥ 1. ∞ (d) 0 P (t, f ) − P ∗ (f ) dt < ∞, where D := supi∈S j ∈S |dij | for any matrix D = [dij ]|S|×|S| . 2), P (t + s, f ) = P (t, f )P (s, f ) = P (s, f )P (t, f ). 2) we obtain (a).

Then a∈A(i) x(i, a) > 0 for all i ∈ S. Define a randomized stationary policy π x by π x (a|i) := x(i, a) b∈A(i) x(i, b) ∀a ∈ A(i) and i ∈ S. 68) Then π x is in Π s , and xπ x (i, a) = x(i, a) for all a ∈ A(i) and i ∈ S. 66) we have xπ (x, a) = 1. 67) and q(j |i, π)μπ (i) = 0 ∀j ∈ S. i∈S Hence, xπ is a feasible solution to D-LP. ˆ > 0}. 64) S ′ is not empty. 68) implies that x(i, a) = π x (a|i)u(i). 69) a∈A(i) q(j |i, a)π x (a|i), substituting u(i) ˆ = 1. 12, u(i) ˆ = μπ x (i) > 0 for all i ∈ S, and so S ′ = S.

The following result establishes a relationship between feasible solutions to the D-LP and randomized stationary policies. 66). Then xπ := {xπ (i, a), a ∈ A(i), i ∈ S} is a feasible solution to the D-LP problem. (b) Let x := {x(i, a), a ∈ A(i), i ∈ S} be a feasible solution to the D-LP problem. Then a∈A(i) x(i, a) > 0 for all i ∈ S. Define a randomized stationary policy π x by π x (a|i) := x(i, a) b∈A(i) x(i, b) ∀a ∈ A(i) and i ∈ S. 68) Then π x is in Π s , and xπ x (i, a) = x(i, a) for all a ∈ A(i) and i ∈ S.

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