Abstract. We consider the problem of computing the value and an optimal strat-egy for minimizing the expected termination time in one-counter Markov deci-sion processes. Since the value may be irrational and an optimal strategy may be rather complicated, we concentrate on the problems of approximating the value up to a given error ε> 0 and computing a finite representation of an ε-optimal strategy. We show that these problems are solvable in exponential time for a given configuration, and we also show that they are computationally hard in the sense that a polynomial-time approximation algorithm cannot exist unless P=NP.
We study the problem of approximation of optimal values in partially-observable Markov decision proc...
We consider partially observable Markov decision processes (POMDPs) with a set of target states and ...
In this report the same situation will be considered as in Hordijk, Dynamic programrrdng and Markov ...
We study the computational complexity of central analysis problems for One-Counter Markov Decision P...
We study the computational complexity of central analysis problems for One-Counter Markov Decision P...
We consider terminating Markov decision processes with imperfect state information. We first assume ...
Markov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decis...
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
In this paper we will consider several variants of the standard successive approximation technique f...
In this paper we will consider several variants of the standard successive approximation technique f...
We study the problem of computing the optimal value function for a Markov decision process with posi...
We consider partially observable Markov decision processes (POMDPs) with a set of target states and ...
summary:In this note we focus attention on identifying optimal policies and on elimination suboptima...
We study the problem of computing the optimal value function for a Markov decision process with posi...
One-counter MDPs (OC-MDPs) and one-counter simple stochastic games (OC-SSGs) are 1-player, and 2-pla...
We study the problem of approximation of optimal values in partially-observable Markov decision proc...
We consider partially observable Markov decision processes (POMDPs) with a set of target states and ...
In this report the same situation will be considered as in Hordijk, Dynamic programrrdng and Markov ...
We study the computational complexity of central analysis problems for One-Counter Markov Decision P...
We study the computational complexity of central analysis problems for One-Counter Markov Decision P...
We consider terminating Markov decision processes with imperfect state information. We first assume ...
Markov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decis...
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
In this paper we will consider several variants of the standard successive approximation technique f...
In this paper we will consider several variants of the standard successive approximation technique f...
We study the problem of computing the optimal value function for a Markov decision process with posi...
We consider partially observable Markov decision processes (POMDPs) with a set of target states and ...
summary:In this note we focus attention on identifying optimal policies and on elimination suboptima...
We study the problem of computing the optimal value function for a Markov decision process with posi...
One-counter MDPs (OC-MDPs) and one-counter simple stochastic games (OC-SSGs) are 1-player, and 2-pla...
We study the problem of approximation of optimal values in partially-observable Markov decision proc...
We consider partially observable Markov decision processes (POMDPs) with a set of target states and ...
In this report the same situation will be considered as in Hordijk, Dynamic programrrdng and Markov ...