Many kinds of education methods should be used because of its particularity,preparing too much for a lesson and too many students from different circles. Therefore,the key to situation and policy class is strengthening and establishing the comprehensive system of teaching management.
Using the method of topology and analysis,we discuss the toplogical structure of Markovian policy classП_(m)~(4) , and simply prove the existence of optimal policies in П_(m)~(4).
In order to promote the abstract level and reusability of policies, a parameterized policy class and an inheritance mechanism between policy classes are introduced.
Generally the β(or (ε,β))- optimal stationary policy is often not unique, and even has as many policies as contained in the stationary policy class, It is natural to hope that a policy with homogeneously (ε-) minimized variance (to the initial states) be found in the β(or (ε, β))- optimal stationary policies.
We investigate the impact of maintenance time variability on system performance and evaluate the performance of various maintenance policies within the proposed policy class when the expected profit rate is maximized.
The discounted Markovian decision programming of our concern consists of statespace and action-set eorresponding to every state, both of whieh are denumerable infinite sets, substochastie transition law family and boundedreward function We have given a succassive approximation method of accelerative eonvergence for (ε-) optimal stationary policy, This algorithm converges to the optimal solution more qurckly than White's successive approximation method. It has also been furnished with a testing criteri...
Using the method of topology and analysis,we discuss the toplogical structure of Markovian policy classП_(m)~(4) , and simply prove the existence of optimal policies in П_(m)~(4). In addition,by the mechanics of introducing Lagrange mutiplicity and mean-value theory,we prove the existence of constraint optimal policies, moreover prove that the constraint optimal policies may be markovian or the convex conbination of two markovian policies.
In this paper, we consider the equivalance of randomized policy class Π and randomized Markov Policy class Πm for non-stationary extensive Markov models { (Sn),(An (i ),i ∈ Sn ), (Pn ), (rn,v)/,For the expected total reward criterion V and average reward criterion , by probability method, we prove that there exists a randomized Markov policy for any randomized policy π,such that v(π) equals V (), equals without any conditions imposed on the factors of the model.