E3S Web Conf.
Volume 229, 2021The 3rd International Conference of Computer Science and Renewable Energies (ICCSRE’2020)
|Number of page(s)||11|
|Published online||25 January 2021|
Discounted Markov Decision Processes with Constrained Costs: the decomposition approach
Laboratory TIAD, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Campus Mghilla, Beni Mellal, Morocco
2 Laboratory of Computer Sciences, Faculty of Sciences Kenitra, IbnTofail University, Morocco
* Corresponding author: email@example.com
In this paper we consider a constrained optimization of discrete time Markov Decision Processes (MDPs) with finite state and action spaces, which accumulate both a reward and costs at each decision epoch. We will study the problem of finding a policy that maximizes the expected total discounted reward subject to the constraints that the expected total discounted costs are not greater than given values. Thus, we will investigate the decomposition method of the state space into the strongly communicating classes for computing an optimal or a nearly optimal stationary policy. The discounted criterion has many applications in several areas such that the Forest Management, the Management of Energy Consumption, the finance, the Communication System (Mobile Networks) and the artificial intelligence.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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