In this paper we formulate Markov Decision Processes with Random Horizon. We show the optimality equation for this problem, however there may not exist optimal stationary strategies. For the MDP (Markov�Decision�Process), with probability distribution for the planning horizon with infinite support, we show Turnpike Planning Horizon Theorem. We develop an algorithm obtaining an optimal first stage decision. We give some numerical examples.En este trabajo formulamos un Proceso de Decisión Markoviano con Horizonte Aleatorio. Desarrollamos la ecuación de optimalidad para este problema, sin embargo puede no existir estrategias optimales estacionarias. Para el MDP (Proceso de Decisión Markoviano), con distribución de probabilidad para horizonte d...
AbstractWe consider a class of problems concerned with maximizing probabilities, given stage-wise ta...
We review a class of online planning algorithms for deterministic and stochastic optimal control pro...
Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose ob...
We study three classes of infinite horizon optimization problems: the undiscounted homogeneous Marko...
summary:This paper is related to Markov Decision Processes. The optimal control problem is to minimi...
Infinite-horizon non-stationary Markov decision processes provide a general framework to model many ...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
We consider the problem of approximating the values and the optimal policies in risk-averse discount...
Planning horizon is a key issue in production planning. Different from previous approaches based on ...
We consider a discrete time Markov Decision Process with infinite horizon. The criterion to be maxim...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
Optimality criteria for Markov decision processes have historically been based on a risk neutral for...
We apply the Target Value Criterion to an MDP with a random planning horizon, derive an optimality e...
Time-average Markov decision problems are considered for the finite state and action spaces. Several...
AbstractWe consider a class of problems concerned with maximizing probabilities, given stage-wise ta...
We review a class of online planning algorithms for deterministic and stochastic optimal control pro...
Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose ob...
We study three classes of infinite horizon optimization problems: the undiscounted homogeneous Marko...
summary:This paper is related to Markov Decision Processes. The optimal control problem is to minimi...
Infinite-horizon non-stationary Markov decision processes provide a general framework to model many ...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
We consider the problem of approximating the values and the optimal policies in risk-averse discount...
Planning horizon is a key issue in production planning. Different from previous approaches based on ...
We consider a discrete time Markov Decision Process with infinite horizon. The criterion to be maxim...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
Optimality criteria for Markov decision processes have historically been based on a risk neutral for...
We apply the Target Value Criterion to an MDP with a random planning horizon, derive an optimality e...
Time-average Markov decision problems are considered for the finite state and action spaces. Several...
AbstractWe consider a class of problems concerned with maximizing probabilities, given stage-wise ta...
We review a class of online planning algorithms for deterministic and stochastic optimal control pro...
Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose ob...