Introduction to reinforcement learning and control theory

Warning

The course is being finalized for the 2025 semester and there may therefore be updates to the reading plan, exercises and projects. I recommend that you use the online version to make sure you are up to date.

Hint

Use ctrl+k to search.

This page contains material and information related to the spring 2025, version of the course Introduction to reinforcement learning and control, offered at DTU.

If you are thinking about taking 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 continuously updated with typos and other adjustments. 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:

02465

Exercise code:

https://lab.compute.dtu.dk/02465material/02465students.git

Course descriptions:

kurser.dtu.dk

Lecture recordings:

panopto.dtu.dk

Discord:

Discord channel (invitation link)

Campus-wide python support:

pythonsupport.dtu.dk

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 31th, 2025	Installation and self-test	Chapter 1-3 , [Her24]		[PDF]
1	Feb 7th, 2025	The finite-horizon decision problem	Chapter 4, [Her24]	1, 2	[PDF]	[1x] [6x]
2	Feb 14th, 2025	Dynamical Programming	Chapter 5-6.2, [Her24]	1, 2	[PDF]	[1x] [6x]
3	Feb 21th, 2025	DP reformulations and introduction to Control	Section 6.3; Chapter 10-11, [Her24]	1, 2	[PDF]	[1x] [6x]
4	Feb 28th, 2025	Discretization and PID control	Chapter 12-14, [Her24]	1, 2	[PDF]	[1x] [6x]
	Mar 6th, 2025	Project 1: Dynamical Programming
5	Mar 7th, 2025	Direct methods and control by optimization	Chapter 15, [Her24]	1	[PDF]	[1x] [6x]
6	Mar 14th, 2025	Linear-quadratic problems in control	Chapter 16, [Her24]	1	[PDF]	[1x] [6x]
7	Mar 21th, 2025	Linearization and iterative LQR	Chapter 17, [Her24]	1	[PDF]	[1x] [6x]
8	Mar 28th, 2025	Exploration and Bandits	Chapter 1; Chapter 2-2.7; 2.9-2.10, [SB18]	1	[PDF]	[1x] [6x]
	Apr 3rd, 2025	Project 2: Control theory
9	Apr 4th, 2025	Bellmans equations and exact planning	Chapter 3; 4, [SB18]	1, 2	[PDF]	[1x] [6x]
10	Apr 11th, 2025	Monte-carlo methods and TD learning	Chapter 5-5.4+5.10; 6-6.3, [SB18]	1	[PDF]	[1x] [6x]
		🥚 🐤 Autumn Holiday 🐤 🥚	🎮	🏖️	🍹
11	Apr 25th, 2025	Model-Free Control with tabular and linear methods	Chapter 6.4-6.5; 7-7.2; 9-9.3; 10.1, [SB18]	1	[PDF]	[1x] [6x]
12	May 2nd, 2025	Eligibility traces	Chapter 10.2; 12-12.7, [SB18]	1	[PDF]	[1x] [6x]
	May 8th, 2025	Project 3: Reinforcement Learning
13	May 9th, 2025	Deep-Q learning	Chapter 6.7-6.9; 8-8.4; 16-16.2; 16.5; 16.6, [SB18]	1	[PDF]	[1x] [6x]

The reading material is available here:

[Her24]:

02465_Notes.pdf

[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	Email
Building B341, auditorium 21	Tue Herlau	tuhe@dtu.dk
Building B341, IT-015	Tue Herlau	tuhe@dtu.dk
Building B341, IT-019	Tue Herlau	tuhe@dtu.dk

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.

Reference name	Download
(Tassa et al. [TET12])	tassa2012.pdf
(Kelly [Kel17])	kelly2017.pdf

Contents

• Information
• Models and Environments
• Exercises
• Projects
• Exam practicals

Indices and tables

• Index

• Module Index

• Search Page

Bibliography

[Her24] (1,2,3,4,5,6,7,8)

Tue Herlau. Sequential decision making. (Freely available online), 2024.

[Kel17]

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.

[SB18] (1,2,3,4,5,6)

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.

[TET12]

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: 22/12/2024 15:10:33