ECTS
2 credits
Training structure
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 fulfill a certain objective
. We will focus on discrete-time Markov decision 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
Know how to model a stochastic control problem in the form of a decision-making Markov process
Know how to implement the dynamic programming algorithm to calculate optimal performance and strategies.
Teaching hours
- Stochastic Control - CMLecture9 a.m.
- Stochastic Control - TutorialTutorials9 a.m.
Mandatory prerequisites
M1 Stochastic Processes Course (Markov Chains)
Gaussian Vectors
Scientific Software (R)
Recommended prerequisites: Optimization and Measurement Theory
Knowledge assessment
Full continuous monitoring of the project
Additional information
Hours:
CM: 9 hours
TD: 9 hours
TP:
Fieldwork: