Abstract. We consider an upper confidence bound algorithm for Markov decision processes (MDPs) with deterministic transitions. For this algorithm we derive upper bounds on the onl...
We consider the problem of evaluating a large number of XPath expressions on an XML stream. Our main contribution consists in showing that Deterministic Finite Automata (DFA) can b...
Todd J. Green, Gerome Miklau, Makoto Onizuka, Dan ...
Abstract— This paper deals with the long run average continuous control problem of piecewise deterministic Markov processes (PDMP‘s) taking values in a general Borel space and ...
We present two new algorithms for finding optimal strategies for discounted, infinite-horizon, Deterministic Markov Decision Processes (DMDP). The first one is an adaptation of...
Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making proble...