Introduction to reinforcement learning and control theory#
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This page contains material and information related to the spring 2024 version of the course Introduction to reinforcement learning and control, offered at DTU.
If you are curious about the course, you can read more about the course here. If you are enrolled and just starting out, you should begin with the Installation. You can find the exercises, project descriptions in the menu to the left.
Practicalities#
Note
This page is automatically updated with typos, etc. I therefore recommend bookmarking it and using the newest version of the exercises.
- Time and place:
Building B341, auditorium 21, 08:00–12:00
- DTU Learn:
- Exercise code:
- Course descriptions:
- Lecture recordings:
- Discord:
- Campus-wide python support:
- Contact:
Tue Herlau, tuhe@dtu.dk.
Course schedule#
The schedule and reading can be found below. Click on the titles to read the exercise and project descriptions.
# |
Date |
Title |
Reading |
Homework |
Exercise |
Slides |
---|---|---|---|---|---|---|
Jan 26th, 2024 |
Chapter 1-3 , [Her24] |
|||||
1 |
Feb 2nd, 2024 |
Chapter 4, [Her24] |
1, 2 |
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2 |
Feb 9th, 2024 |
Chapter 5-6.2, [Her24] |
1, 2 |
|||
3 |
Feb 16th, 2024 |
Section 6.3; Chapter 10-11, [Her24] |
1, 2 |
|||
4 |
Feb 23th, 2024 |
Chapter 12-14, [Her24] |
1, 2 |
|||
Feb 29th, 2024 |
||||||
5 |
Mar 1st, 2024 |
Chapter 15, [Her24] |
1 |
|||
6 |
Mar 8th, 2024 |
Chapter 16, [Her24] |
1 |
|||
7 |
Mar 15th, 2024 |
Chapter 17, [Her24] |
1 |
|||
8 |
Mar 22th, 2024 |
Chapter 1; Chapter 2-2.7; 2.9-2.10, [SB18] |
1 |
|||
Apr 4th, 2024 |
||||||
9 |
Apr 5th, 2024 |
Chapter 3; 4, [SB18] |
1, 2 |
|||
10 |
Apr 12th, 2024 |
Chapter 5-5.4+5.10; 6-6.3, [SB18] |
1 |
|||
11 |
Apr 19th, 2024 |
Chapter 6.4-6.5; 7-7.2; 9-9.3; 10.1, [SB18] |
1 |
|||
12 |
Apr 26th, 2024 |
Chapter 10.2; 12-12.7, [SB18] |
1 |
|||
May 2nd, 2024 |
||||||
13 |
May 3rd, 2024 |
Chapter 6.7-6.9; 8-8.4; 16-16.2; 16.5; 16.6, [SB18] |
1 |
The reading material is available here:
- [Her24]:
- [SB18]:
Introduction to Reinforcement Learning (2020) (Authors homepage)
You can find the exam QA slides here. Details about the exam QA session will be announced on DTU Learn.
Note
Chapters 1–3 is background information about python and are therefore not part of the main course content (pensum). Knowledge of python is required for the exams.
The Homework column list those problems that will be discussed during class. They are also indicated by a in the margin of the PDF file. I encourage you to prepare them at home and present your solution during the exercise session.
Exercise sessions#
Hint
I will upload solutions to some of the python problems on gitlab.
The teaching assistants will be available Fridays 10:00–12:00 after the lecture.
Location |
Instructor |
|
---|---|---|
Building B341, IT-019 |
Calle Ryge Carlsen |
|
Building B341, IT-015 |
Karl Meisner-Jensen |
|
Victor Tolsager Olesen |
||
Building B341, auditorium 21 |
Tue Herlau |
For the exercises, you are encouraged to prepare the homework problems at home (see syllabus above), and present your solution during the exercise session.
Additional reading material#
The material below is referenced in the course but it is not part of the course syllabus.
Contents#
Indices and tables#
Bibliography#
Matthew Kelly. An introduction to trajectory optimization: how to do your own direct collocation. SIAM Review, 59(4):849–904, 2017. (See kelly2017.pdf). URL: https://epubs.siam.org/doi/pdf/10.1137/16M1062569.
Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. (Freely available online). URL: http://incompleteideas.net/book/the-book-2nd.html.
Yuval Tassa, Tom Erez, and Emanuel Todorov. Synthesis and stabilization of complex behaviors through online trajectory optimization. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 4906–4913. IEEE, 2012. (See tassa2012.pdf). URL: https://ieeexplore.ieee.org/abstract/document/6386025.
This page was last updated at:
>>> from datetime import datetime
>>> print("Document updated at:", datetime.now().strftime("%d/%m/%Y %H:%M:%S"))
Document updated at: 22/08/2024 12:09:12