• 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.

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Objectives

Model a stochastic control problem as a Markov decision process
Implement the dynamic programming algorithm to calculate optimal performance and strategies.

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Necessary prerequisites

M1 stochastic processes course (Markov chains)
Gaussian vectors
Scientific software (R)
 


Recommended prerequisites: Optimization and measurement theory

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Knowledge control

Project-based continuous assessment

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Further information

Timetable:
CM: 9h
TD: 9h
TP:
Field :

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