ECTS
2 credits
Component
Faculty of Science
Description
This course is an introduction to stochastic control. In this type of
problem, we try to modify the natural trajectory of a process to meet a certain
objective. We will place ourselves in the framework of discrete-time decisional Markov processes where we can
choose an action at each time step. We will see how to formalize the stochastic control problems in this framework, and how to solve them theoretically and numerically.
Objectives
Know how to model a stochastic control problem in the form of a Markov decision process
Know how to implement the dynamic programming algorithm to calculate the optimal performances and strategies.
Necessary pre-requisites
M1 stochastic process course (Markov chains)
Gaussian vectors
Scientific software (R)
Recommended Prerequisites: Optimization and Measurement Theory
Knowledge control
Continuous control on project
Additional information
Hourly volumes:
CM : 9h
TD : 9h
TP :
Field :