We consider decentralized control of Markov decision processes and give complexity bounds on the worst-case running time for algorithms that find optimal solutions. Generalization...
Daniel S. Bernstein, Shlomo Zilberstein, Neil Imme...
Abstract. In parametric Markov Decision Processes (PMDPs), transition probabilities are not fixed, but are given as functions over a set of parameters. A PMDP denotes a family of ...
rexample Guided Abstraction-Refinement Framework for Markov Decision Processes ROHIT CHADHA and MAHESH VISWANATHAN Dept. of Computer Science, University of Illinois at Urbana-Champ...
Abstract. We consider a control problem where the decision maker interacts with a standard Markov decision process with the exception that the reward functions vary arbitrarily ove...
Formal analysis of decentralized decision making has become a thriving research area in recent years, producing a number of multi-agent extensions of Markov decision processes. Wh...