Add to ORKGA linear programming approach to constrained nonstationary infinite-horizon Markov decision processe
The Markov decision process is treated in a variety of forms or cases: finite or infinite horizon, w...
Many real world problems with time-varying characteristic and unbounded horizon can be modeled as an...
International audienceWe study in this paper a multiobjective dynamic programm-ming where all the cr...
This paper treats a Markov decision problem with an infinite planning horizon and no discounting. Th...
We study infinite-horizon nonstationary Markov decision processes with discounted cost criterion, fi...
Linear Programming is known to be an important and useful tool for solving Markov Decision Processes...
We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state an...
Cover title.Includes bibliographical references.Supported by the National Science Foundation. NSF-EC...
In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary obje...
Infinite-horizon non-stationary Markov decision processes provide a general framework to model many ...
AbstractWe relate average optimal stationary policies in countable space Markov decision processes a...
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
We introduce and study a class of non-stationary semi-Markov decision processes on a finite horizon....
International audienceThis paper deals with discrete-time Markov Decision Processes (MDP's) under co...
The concept of partially observable Markov decision processes was born to handle the problem of lack...
The Markov decision process is treated in a variety of forms or cases: finite or infinite horizon, w...
Many real world problems with time-varying characteristic and unbounded horizon can be modeled as an...
International audienceWe study in this paper a multiobjective dynamic programm-ming where all the cr...
This paper treats a Markov decision problem with an infinite planning horizon and no discounting. Th...
We study infinite-horizon nonstationary Markov decision processes with discounted cost criterion, fi...
Linear Programming is known to be an important and useful tool for solving Markov Decision Processes...
We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state an...
Cover title.Includes bibliographical references.Supported by the National Science Foundation. NSF-EC...
In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary obje...
Infinite-horizon non-stationary Markov decision processes provide a general framework to model many ...
AbstractWe relate average optimal stationary policies in countable space Markov decision processes a...
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
We introduce and study a class of non-stationary semi-Markov decision processes on a finite horizon....
International audienceThis paper deals with discrete-time Markov Decision Processes (MDP's) under co...
The concept of partially observable Markov decision processes was born to handle the problem of lack...
The Markov decision process is treated in a variety of forms or cases: finite or infinite horizon, w...
Many real world problems with time-varying characteristic and unbounded horizon can be modeled as an...
International audienceWe study in this paper a multiobjective dynamic programm-ming where all the cr...