By Topic

Optimal control of average reward constrained continuous-time finite Markov decision processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
1 Author(s)
Feinberg, E.A. ; Dept. of Appl. Math. & Stat., State Univ. of New York, Stony Brook, NY, USA

The paper studies the optimization of average-reward continuous-time finite state and action Markov decision processes with multiple criteria and constraints. Under the standard unichain assumption, we prove the existence of optimal K-switching strategies for feasible problems with K constraints. For switching randomized strategies, the decisions depend on the current state and the time spent in the current state after the last jump. For stationary strategies, these functions do not depend on sojourn times, i.e., they are constant in time. For K-switching strategies, these functions are piecewise constant and the total number of jumps is limited by K. If there is no absorbing states, there exist also optimal K-randomized policies. We consider the linear programming approach and provide algorithms for calculations of optimal policies.

Published in:

Decision and Control, 2002, Proceedings of the 41st IEEE Conference on  (Volume:4 )

Date of Conference:

10-13 Dec. 2002