Let (Xn) be a Markov process (in discrete time) with I state space E, I transition kernel Qn(·|x). Let (Xn) be a controlled Markov process with I state space E, action space A, I admissible state-action pairs Dn ⊂ E × A, I transition kernel Qn(·|x,a). A decision An at time n is in general σ(X1,...,Xn)-measurable. However, Markovian structure implies An = fn(Xn) is sufficient. Markov Decision Processes with Applications to Finance MDPs with Finite Time Horizo
Gottinger HW. Markovian decision processes with limited state observability and unobservable costs. ...
International audienceThis book presents the first part of a planned two-volume series devoted to a ...
International audienceThis book presents the first part of a planned two-volume series devoted to a ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
The theory of Markov Decision Processes is the theory of controlled Markov chains. Its origins can b...
We are interested in the existence of pure and stationary optimal strategies in Markov decision proc...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov decision processes (MDPs) are models of dynamic decision making under uncertainty. These mode...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
This paper analyzes a connection between risk-sensitive and minimax criteria for discrete-time, fini...
Markov chains1 and Markov decision processes (MDPs) are special cases of stochastic games. Markov ch...
We consider a discrete time Markov Decision Process with infinite horizon. The criterion to be maxim...
This note considers finite state and action spaces controlled Markov chains with multiple costs. The...
We present a class of metrics, defined on the state space of a finite Markov decision process (MDP)...
Optimality criteria for Markov decision processes have historically been based on a risk neutral for...
Gottinger HW. Markovian decision processes with limited state observability and unobservable costs. ...
International audienceThis book presents the first part of a planned two-volume series devoted to a ...
International audienceThis book presents the first part of a planned two-volume series devoted to a ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
The theory of Markov Decision Processes is the theory of controlled Markov chains. Its origins can b...
We are interested in the existence of pure and stationary optimal strategies in Markov decision proc...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov decision processes (MDPs) are models of dynamic decision making under uncertainty. These mode...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
This paper analyzes a connection between risk-sensitive and minimax criteria for discrete-time, fini...
Markov chains1 and Markov decision processes (MDPs) are special cases of stochastic games. Markov ch...
We consider a discrete time Markov Decision Process with infinite horizon. The criterion to be maxim...
This note considers finite state and action spaces controlled Markov chains with multiple costs. The...
We present a class of metrics, defined on the state space of a finite Markov decision process (MDP)...
Optimality criteria for Markov decision processes have historically been based on a risk neutral for...
Gottinger HW. Markovian decision processes with limited state observability and unobservable costs. ...
International audienceThis book presents the first part of a planned two-volume series devoted to a ...
International audienceThis book presents the first part of a planned two-volume series devoted to a ...