Discrete-time stochastic processes

B show that every nite state markov chain contains at

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Unformatted text preview: ichain stationary policies, until eventually one of them satisfies Bellman’s equation and is thus optimal. Lemma 4.2. Let k = (k1 , . . . , kM ) be an arbitrary stationary policy in an inherently recurrent Markov decision problem. Let R be a recurrent class of states in k. Then a unichain ˜ ˜ ˜ ˜ stationary policy k = (k1 , . . . , kM ) exists with the recurrent class R and with kj = kj for j ∈ R. Proof: Let j be any state in R. By the inherently recurrent assumption, there is a decision vector, say k 0 under which j is accessible from all other states (see Exercise 4.38). Choosing 0 ˜ ˜ ki = ki for i ∈ R and ki = ki for i ∈ R completes the proof. / Now that we are assured that unichain stationary policies exist and can be found, we can state the policy improvement algorithm for inherently recurrent Markov decision problems. This algorithm is a generalization of Howard’s policy iteration algorithm, [How60]. Policy Improvement Algorithm 1. Choose an arbitrary unichain policy k 0 0 0 2. For policy k 0 , calculate w 0 and g 0 from w 0 + g 0 e = r k + [P k ]w 0 . 3. If w 0 + g 0 e = maxk {r k + [P k ]w 0 }, then stop; k 0 is optimal. P (k ) 0 (k ) 0 0 4. Otherwise, choose i and ki so that wi + g 0 < ri i + j Pij i wj . For j 6= i, let kj = kj . 5. If the policy k = (k1 , . . . kM ) is not a unichain, then let R be the recurrent class in ˜ policy k that contains state i, and let k be the unichain policy of Lemma 4.2. Update ˜. k to the value of k 174 CHAPTER 4. FINITE-STATE MARKOV CHAINS 6. Update k 0 to the value of k and return to step 2. (k ) 0 If the stopping test in step 3 fails, then there is some i for which wi + g 0 < maxki {ri i + P (ki ) 0 j Pij wj }, so step 4 can always b e executed if the algorithm do es not stop in step 3. The resulting policy k then satisfies w 0 + g0 e ≤ 6= r k + [P k ]w 0 , (4.70) where ≤ means that the inequality is strict for at least one component (namely i) of the 6= vectors. 0 Note that at the end of step 4, [P k ] differs from [P k ] only in the transitions out of state i. Thus the set of states from which i is accessible is the same in k 0 as k . If i is recurrent in the unichain k 0 , then it is accessible from all states in k 0 and thus also accessible from all states in k . It follows that i is also recurrent in k and that k is a unichain (see Exercise 4.2. On the other hand, if i is transient in k 0 , and if R0 is the recurrent class of k 0 , then R0 must also be a recurrent class of k , since the transitions from states in R0 are unchanged. There (k ) are then two possibilities when i is transient in k 0 . First, if the changes in Pij i eliminate all the paths from i to R0 , then a new recurrent class R will be formed with i a member. This is the case in which step 5 is used to change k back to a unichain. Alternatively, if a path still exists to R0 , then i is transient in k and k is a unichain with the same recurrent class R0 as k 0 . These results are summarized in the following lemma: Lemma 4.3. There are only three possibilities for k at the end of step 4 of the policy improvement algorithm for inherently recurrent Markov decision problems. First, k is a unichain and i is recurrent in both k0 and k. Second, k is not a unichain and i is transient in k0 and recurrent in k. Third, k is a unichain with the same recurrent class as k0 and i is transient in both k0 and k. The following lemma now asserts that the new policy on returning to step 2 of the algorithm is an improvement over the previous policy k 0 . Lemma 4.4. Let k0 be the unichain policy of step 2 in an iteration of the policy improvement algorithm for an inherently recurrent Markov decision problem. Let g 0 , w0 , R0 be the gain per stage, relative gain vector, and recurrent class respectively of k0 . Assume the algorithm doesn’t stop at step 3 and let k be the unichain policy of step 6. Then either the gain per stage g of k satisfies g > g 0 or else the recurrent class of k is R0 , the gain per stage satisfies g = g 0 , and there is a shifted relative gain vector, w, of k satisfying w0 ≤ 6= w 0 and wj = wj for each j ∈ R0 . (4.71) Proof*: The policy k of step 4 satisfies (4.70) with strict inequality for the component i in which k 0 and k differ. Let R be any recurrent class of k and let π be the steady-state probability vector for R. Premultiplying both sides of (4.70) by π , we get π w 0 + g 0 ≤ π r k + π [P k ]w 0 . (4.72) 4.6. MARKOV DECISION THEORY AND DYNAMIC PROGRAMMING 175 Recognizing that π [P k ] = π and cancelling terms, this shows that g 0 ≤ π r k . Now (4.70) is satisfied with strict inequality for component i, and thus, if πi > 0, (4.72) is satisfied with strict inequality. Thus, g 0 ≤ π r k with equality iff πi = 0. (4.73) For the first possibility of Lemma 4.3, k is a unichain and i ∈ R. Thus g 0 < π r k = g . Similarly, for the second possibility in Lemma 4.3, i ∈ R for the new recurrent class that is ˜ formed in k ,...
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This note was uploaded on 09/27/2010 for the course EE 229 taught by Professor R.srikant during the Spring '09 term at University of Illinois, Urbana Champaign.

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