We introduce a new methodology for the exact analysis of M/G/1-type Markov processes. The methodology uses basic, well-known results for Markov chains by exploiting the structure ...
This paper investigates the decentralized detection of Hidden Markov Processes using the NeymanPearson test. We consider a network formed by a large number of distributed sensors....
Joffrey Villard, Pascal Bianchi, Eric Moulines, Pa...
We consider how state similarity in average reward Markov decision processes (MDPs) may be described by pseudometrics. Introducing the notion of adequate pseudometrics which are we...
This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto...
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful ...