Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

Option pricing and stochastic control methods constitute a vital intersection of quantitative finance and applied mathematics, offering robust frameworks for evaluating derivative securities and ...

The Annals of Applied Probability, Vol. 28, No. 1 (February 2018), pp. 1-34 (34 pages) In this paper, we aim to develop the stochastic control theory of branching diffusion processes where both the ...

We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, ...