Stochastic control theory. Dynamic programming principle
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Moreover, an application to the latter method to the very popular class of mean-field games MFGs will be presented as well together with some relevant applications to study strategic interaction among economic agents, such as banks and investors. Other applications of MFGs to, for instance, energy markets and crowd dynamics, will be also discussed if time allows.
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The list of topics will be: i Stochastic control: formulation of the problem with examples. Springer Nature. Continuous-time stochastic control and optimization with financial applications Vol.
Optimal stochastic control, stochastic target problems, and back- ward SDE Vol. Other more specific references will be given during the lectures when needed. Time-table Monday, 29th of April: Toggle navigation. English Italiano.
This topic is very close to the subject of stochastic approximation algorithms. Finally, in the last part of the course we cover some additional techniques developed specifically for the suboptimal control of complex dynamical networks.
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Students should have some knowledge of probability theory and Markov chains, roughly at the level of [BT08]. Prior exposure to control theory and optimization is recommended. They can expect a number of assignments to require implementing algorithms studied in class. The project will also require a computational component. Date Topic Reading Assignments W. The Dynamic Programming Algorithm.
Pset 2 out M. Intro to "Suboptimal Control".
Pset 3 out M. Intro to Discounted infinite horizon DP. Value Iteration. PS4 out M.
Midterm out. The main reference for the course. Bertsekas and J.
Tsitsiklis, "Introduction to Probability", 2nd edition, Athena Scientific, Puterman, "Markov Decision Processes", Wiley, A classic reference. Sutton and A.
AKVFM stochastic control theory | TU Wien
Research Monographs and Papers. Bellman and E.
Bertsekas and S. Borkar and S. Meyn, "The O.