Textbook:
Markov Decision Processes and Stochastic Positional Games: Optimal Control on Complex Networks
Author(s): Dmitrii Lozovanu
Institute of Mathematics and CS Moldowa Academy of Science Chisinau, Moldova
Stanford University, Stanford, CA, USA

Course description:
This course presents  findings and results concerning the solutions of especially finite state-space Markov decision problems and determining Nash equilibria for related stochastic games with average and total expected discounted reward payoffs. In addition, it focuses on a new class of stochastic games: stochastic positional games that extend and generalize the classic deterministic positional games. It presents new algorithmic results on the suitable implementation of quasi-monotonic programming techniques. Moreover, the textbook presents applications of positional games within a class of multi-objective discrete control problems and hierarchical control problems on networks.
Given its scope, the textbook will benefit all researchers and graduate students who are interested in Markov theory, control theory, optimization and games.