ECTS
2 credits
Training Structure
College of Sciences
Description
This course provides an introduction to stochastic control. In this type of
problem, the goal is to modify the natural trajectory of a process to achieve a certain
objective. We will focus on discrete-time decision-making Markov processes, in which we can
choose an action at each time step. We will explore how to formalize stochastic control problems within this framework and how to solve them both theoretically and numerically.
Objectives
Be able to model a stochastic control problem as a decision Markov process
Be able to implement the dynamic programming algorithm to calculate optimal performance and strategies.
Class Hours
- Stochastic Control - CMLecture9:00 a.m.
- Stochastic Control - TutorialTutorials9:00 a.m.
Mandatory Prerequisites
Master's Level 1 Course on Stochastic Processes (Markov Chains)
Gaussian Vectors
Scientific Software (R)
Recommended Prerequisites: Optimization and Measure Theory
Knowledge Assessment
Comprehensive Ongoing Assessment Based on Projects
Additional Information
Hours:
Lectures: 9 hours
Tutorials: 9 hours
Lab:
Fieldwork: