ECTS
2 credits
Component
Faculty of Science
Description
This course is an introduction to stochastic control. In this type of
problem, we seek to modify the natural trajectory of a process in order to achieve a certain
objective. We will consider discrete-time decisional Markov processes, where we can
choose an action at each time step. We will see how to formalize stochastic control problems in this framework, and how to solve them theoretically and numerically.
Objectives
Model a stochastic control problem as a Markov decision process
Implement the dynamic programming algorithm to calculate optimal performance and strategies.
Necessary prerequisites
M1 stochastic processes course (Markov chains)
Gaussian vectors
Scientific software (R)
Recommended prerequisites: Optimization and measurement theory
Knowledge control
Project-based continuous assessment
Further information
Timetable:
CM: 9h
TD: 9h
TP:
Field :