Institute for Numerical Simulation
Rheinische Friedrich-Wilhelms-Universität Bonn
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Lecture course in sommer semester 2017:

V4E2 - Numerical Simulation
Optimal Control and Reinforcement Learning

Prof. Dr. Jochen Garcke

Assistent: Glenn Byrenheid

Location: Room 6.020, Wegelerstr. 6
Time: Tuesday, 10:15 - 11:45
Thursday, 8:30 - 10:00
Exercise: Thursday, 10:15-11:45
Office hours: on appointment, e-mail garcke.ins.uni-bonn.de

Content of the lecture:

News:

Attached you can find corrected remarks for the end of the proof of Theorem 45.

Exercise sheets:

The homework has to be handed in at the beginning of the following exercise. Not each student separately hands in his/her solutions, but you form groups of two students, which hand in one written report together. Each student needs to at least once present solutions in the exercise.
Nr: Link: Due Remarks and errata
1 sheet_1.pdf 27.04.2017
2 sheet_2.pdf 04.05.2017
3 sheet_3_2.pdf 11.05.2017 Exercise 1. Assume additionally the (uniform) continuity of the Hamiltonian H in the first and second variable
4 sheet_4.pdf 18.05.2017
5 sheet_5.pdf 30.05.2017 To be handed in on Tuesday, 30.05.2017, after the lecture.
6 sheet_6_2.pdf 13.06.2017 Exercise 1 updated. Opportunity to reinsert it with Sheet 7.
7 sheet_7.pdf 20.06.2017
8 sheet_8.pdf 27.06.2017
9 sheet_9.pdf 04.07.2017
10 sheet_10.pdf 13.07.2017 Exercise 2 updated. Assumption concering global extreme points added.
11 sheet_11.pdf 20.07.2017 Exercise 1 updated.

Prerequisites:

The content of the two lectures on Numerische Mathematik from the second year of the bachelor studies are expected. In particular knowledge of (nonlinear) optimization and numerical methods for ODEs is recommended, the (German) lecture notes from the course in 2015 are available on request. Furthermore, (Lagrange) interpolation is expected, function discretization by finite elementes is helpful, although for HJB-equations one cannot use the mathematical ideas from the field of numerical solution of PDEs (e.g. Sobolev spaces or Galerkin methods do not play a role here). Parts of the prerequisites will be freshened up in the exercises. In the second half we might do some numerical exercises / experiments for reinforcement learning using existing python-based frameworks.

Exams:

The oral exams will take place between 01.08.17 and 04.08.17. Admittance for oral exam based on homework assignments requiring 50% of the points from the exercise sheets.

Selected Literature: