Add to ORKGWe describe an extension of the Markov decision process model in which a continuous time dimension is included in the state space. This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public transportation and telescope observation scheduling
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), part...
We introduce TMDPpoly, an algorithm designed to solve planning problems with durative actions, under...
We describe an extension of the Markov decision process model in which a continuous time dimension i...
The main focus of this thesis is Markovian decision processes with an emphasis on incorporating time...
We propose a novel approach for solving continuous and hybrid Markov Decision Processes (MDPs) based...
We consider a decision-making problem where the environment varies both in space and time. Such prob...
Solving Markov decision processes (MDPs) with con-tinuous state spaces is a challenge due to, among ...
We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, unde...
Address email We present an approximation scheme for solving Markov Decision Processes (MDPs) in whi...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
We define TTD-MDPs, a novel class of Markov decision processes where the traditional goal of an agen...
We define TTD-MDPs, a novel class of Markov decision processes where the traditional goal of an agen...
This note addresses the time aggregation approach to ergodic finite state Markov decision processes ...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), part...
We introduce TMDPpoly, an algorithm designed to solve planning problems with durative actions, under...
We describe an extension of the Markov decision process model in which a continuous time dimension i...
The main focus of this thesis is Markovian decision processes with an emphasis on incorporating time...
We propose a novel approach for solving continuous and hybrid Markov Decision Processes (MDPs) based...
We consider a decision-making problem where the environment varies both in space and time. Such prob...
Solving Markov decision processes (MDPs) with con-tinuous state spaces is a challenge due to, among ...
We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, unde...
Address email We present an approximation scheme for solving Markov Decision Processes (MDPs) in whi...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
We define TTD-MDPs, a novel class of Markov decision processes where the traditional goal of an agen...
We define TTD-MDPs, a novel class of Markov decision processes where the traditional goal of an agen...
This note addresses the time aggregation approach to ergodic finite state Markov decision processes ...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), part...
We introduce TMDPpoly, an algorithm designed to solve planning problems with durative actions, under...