In this paper,we use the optimal equation with expected state transitions to approximate the optimal equation of MDP average model,and find a general method by which we can determine the error bounds of the solutions of the two optimal equations.
In this paper,using the generalization of the fixed point theorem for cont-ractions,we set up the optimal equation for non-stationary MDP with the aver-age criterion and supply the sufficent conditions under which either the optimalor ε-optimal polices exists.
Among 2 555 selection index equations with different numbers of traits(1≤number of traits ≤9) which were constituted with 22 traits in groups, the optimal equation was constituted with three agronomical traits,i. e. the length of growth durtion,the plant height and grain number per panicle on main stem.
In this paper, We consider the denumable state space non- stationary MDP average model with incomplete information, By the translation of the model, We build up a optimal equation (OE) for the MDP average Model with incomplete Information, and also give the condition under which the solution of OE and the ε-optimal policies must exist.
Based on performance potential theorem and Bellman optimality equation, it is easy to establish optimality equation, which we call performance potential-based Bellman optimality equation, for both average-cost and discounted-cost performance criteria.
Through the transformation of models, the semi-Markov decision programming and the continuous time MDP are transformed to the discrete time MDP respectively, with the optimality equations kept equivalent, so that the most results in the discrete time MDP can be extended to the two other MDP models.
A nonstationary Markov decision processes with average cost is investigated in the case of the general state space. The results of the optimality equations for average cost established by the optimality equations of a complement discounted model under the case of stationary are extended to the case of nonstationary. By use of this result,the existence of an optimal policy is proved.
In this paper, a non-stationary discounted Markovian Decision model with unbounded rewards is investigated, in which the discount factor β_t is dependent of the state and the action taken before last step of the system, under some assumptions, the optimality equations are established, and the existence of an ε-optimal policy is proved.
This paper first investigates the finited horizon non-Markovican decision processes,where the transition probabilities have no longer Markov property. The optimality equations for the model are extablished. The existeme of ε optimal policies is proved.
In this stochastic stopping model, we prove that there exists an optimal deterministic and stationary policy and the optimality equation has a unique solution.
It is shown that both value functions satisfy the optimality equation and upper and lower bounds as well as conditions for equality for these functions are presented.
Under a Lyapunov function condition, we show that stationary policies obtained from the average reward optimality equation are not only average reward optimal, but indeed sample path average reward optimal, for almost all sample paths.
For the case of the switching arms, only of one which creates rewards, we solve explicitly the average optimality equation and prove that a myopic policy is average optimal.
We establish also a lexicographical policy improvement algorithm leading to Blackwell optimal policies and the relation between such policies and the Blackwell optimality equation.
An analogy between the optimality equations and the governing equations for a set of certain static beams permits obtaining numerical solutions to the optimal control problem with the help of standard 'structural' FEM software.
From the optimality equations which are provided in this paper, we translate the average variance criterion into a new average expected cost criterion.
Controlled Markov chains with risk-sensitive criteria: Average cost, optimality equations, and optimal solutions
The approach uses an analogy between the optimality equations for control in the time domain and the governing equations for a set of static beams in the spatial domain.
Moreover,necessary andsufficient conditions are given so that the optimality equations have a bounded solution with an additional property.
Wheat cultivars of different genotypes selected from different places showed in a three year experiment a common law in their milking stage that the accumulation of dry material during this stage was done at a slow—fast—slow speed, in accordance with a "S" type distribution. Each cultivar was found to have a specific peak milking stage, during which the entrance of nutrition into grains tended to follow an indicial equation of y=ab~x. Several evironmental factors affecting milking were analysed by multiple regression...
Wheat cultivars of different genotypes selected from different places showed in a three year experiment a common law in their milking stage that the accumulation of dry material during this stage was done at a slow—fast—slow speed, in accordance with a "S" type distribution. Each cultivar was found to have a specific peak milking stage, during which the entrance of nutrition into grains tended to follow an indicial equation of y=ab~x. Several evironmental factors affecting milking were analysed by multiple regression method and an optimum equation was suggested for the main factors affecting grain weight. The relationship between the source, distribution, and pool was studied. Some cultivars with goodmilking were selected out.