Average Reward Criterion

1989 ◽  
pp. 51-82
Author(s):  
O. Hernández-Lerma
Keyword(s):  
Average Reward ◽  
Average Reward Criterion ◽  
Reward Criterion

scholarly journals Sample-Path Optimal Stationary Policies in Stable Markov Decision Chains with the Average Reward Criterion

2015 ◽  
Vol 52 (2) ◽  
pp. 419-440
Author(s):  
Rolando Cavazos-Cadena ◽  
Raúl Montes-De-Oca ◽  
Karel Sladký
Keyword(s):  
Sample Path ◽  
Point Of View ◽  
Average Reward ◽  
Stationary Policy ◽  
Optimality Equation ◽  
Markov Decision ◽  
Average Reward Criterion ◽  
Compact Action Sets ◽  
Path Point ◽  
Reward Criterion

This paper concerns discrete-time Markov decision chains with denumerable state and compact action sets. Besides standard continuity requirements, the main assumption on the model is that it admits a Lyapunov function ℓ. In this context the average reward criterion is analyzed from the sample-path point of view. The main conclusion is that if the expected average reward associated to ℓ2 is finite under any policy then a stationary policy obtained from the optimality equation in the standard way is sample-path average optimal in a strong sense.

scholarly journals Fuzzy decision processes with an average reward criterion

1999 ◽  
Vol 30 (7-8) ◽  
pp. 7-20
Author(s):  
M. Kurano ◽  
M. Yasuda ◽  
J.-I. Nakagami ◽  
Y. Yoshida
Keyword(s):  
Decision Processes ◽  
Average Reward ◽  
Fuzzy Decision ◽  
Average Reward Criterion ◽  
Reward Criterion

On the undiscounted tax problem with precedence constraints

1996 ◽  
Vol 28 (4) ◽  
pp. 1123-1144
Author(s):  
K. D. Glazebrook
Keyword(s):  
Single Machine ◽  
Precedence Constraints ◽  
Sensitivity Analyses ◽  
Average Reward ◽  
State Dependent ◽  
Index Policies ◽  
Average Reward Criterion ◽  
Reward Criterion

A single machine is available to process a collection of jobs J, each of which evolves stochastically under processing. Jobs incur costs while awaiting the machine at a rate which is state dependent and processing must respect a set of precedence constraints Γ. Index policies are optimal in a variety of scenarios. The indices concerned are characterised as values of restart problems with the average reward criterion. This characterisation yields a range of efficient approaches to their computation. Index-based suboptimality bounds are derived for general processing policies. These bounds enable us to develop sensitivity analyses and to evaluate scheduling heuristics.

Semi-Markov decision processes with polynomial reward

1982 ◽  
Vol 19 (2) ◽  
pp. 301-309 ◽  
Author(s):  
Zvi Rosberg
Keyword(s):  
Transition Period ◽  
Queueing Network ◽  
Decision Processes ◽  
Average Reward ◽  
Network Scheduling ◽  
Long Run ◽  
Markov Decision ◽  
Average Reward Criterion ◽  
Long Run Average Reward ◽  
Reward Criterion

A semi-Markov decision process, with a denumerable multidimensional state space, is considered. At any given state only a finite number of actions can be taken to control the process. The immediate reward earned in one transition period is merely assumed to be bounded by a polynomial and a bound is imposed on a weighted moment of the next state reached in one transition. It is shown that under an ergodicity assumption there is a stationary optimal policy for the long-run average reward criterion. A queueing network scheduling problem, for which previous criteria are inapplicable, is given as an application.

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