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Before choosing course

Course offering missing for current semester as well as for previous and coming semesters
* Retrieved from Course syllabus FEL3260 (Spring 2012–)

Content and learning outcomes

Course contents

Overview and examples of sequential decision problems.

PART I - Stochastic models

1. Review of essential probabilistic tools. Markov chains, Martingales, basic concentration inequalities.

2. Discrete time Markov Decision Processes (MDPs).

                             2a. Finite time-horizon. Principle of optimality, backward induction.

                             2b. Infinite time-horizon. Principle of optimality, value / policy iteration, modified                      policy iteration, linear programming.  

3. Solving MDPs - part 1. Exact solutions based on structural properties of the MDP.

4. Solving MDPs - part 2. Some approximation methods.

5. Extensions. Constrained MDPs, Partially Observable MDPs, Decentralized MDPs.

6. Limit theorems. Going from MDPs to deterministic continuous-time control and back.

7. Optimal stopping time problems.

8. Kalman filter.

9. Prediction with expert advice and Multi-Armed Bandit (MAB) problems.

PART II - Adversarial models and Games.

1. Prediction with expert advice and MAB problems in adversarial scenarios.

2. Sequential decision making in games. Internal regret, Correlated equilibria, Convergence to and selection of Nash Equilibria.

3. Recent advances in online optimization.

Intended learning outcomes

After completing this course, students should be able to rigorously formulate and classify sequential decision problems, to estimate their tractability, and to propose and efficiently implement methods towards their solutions.

Course Disposition

Lectures, exercices, presentations on selected topics by participants, homework problems, projects (small group)

Literature and preparations

Specific prerequisites

N/A

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

A list of books will be provided, but the lecture notes provided will contain all key concepts and points developed during the course.  

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

    Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

    The examiner may apply another examination format when re-examining individual students.

    N/A

    Other requirements for final grade

    ·         25%: 20 min presentation in one of the lectures

    ·         25%: Solutions to homework problems

    ·         50%: Project (the project may be conducted either alone or in pair, and could be related to the students’ own research problems)

    Opportunity to complete the requirements via supplementary examination

    No information inserted

    Opportunity to raise an approved grade via renewed examination

    No information inserted

    Examiner

    Profile picture Alexandre Proutiere

    Ethical approach

    • All members of a group are responsible for the group's work.
    • In any assessment, every student shall honestly disclose any help received and sources used.
    • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

    Further information

    Course web

    No information inserted

    Offered by

    EECS/Decision and Control Systems

    Main field of study

    No information inserted

    Education cycle

    Third cycle

    Add-on studies

    No information inserted

    Contact

    Alexandre Proutiere

    Postgraduate course

    Postgraduate courses at EECS/Decision and Control Systems