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 study the problem of scheduling a set of J jobs on M machines with stochastic job processing times when no preemptions are allowed and with a weighted sum of expected completion times objective.