Admission requirements
It is strongly recommended that the student:
has successfully completed the master courses ‘Reinforcement Learning’ and ‘Introduction to Deep Learning’.
is interested in scientific research.
is proficient in programming, machine learning, presenting and report writing.
Warnings:
The course has a limited number of available spots. Students should check availability and sign up by contacting the responsible teacher in advance.
Peer feedback is an important aspect of the course, and presence at the weekly meetings is mandatory.
Description
For detailed information please visit the course website.
Deep Reinforcement Learning (RL) is a field of artificial intelligence that has attracted much attention, for example being the first computer programme to beat the human world champion in the game of Go. In this seminar we will dive into recent deep reinforcement learning papers.
The structure of the course is as follows. We first jointly read up on the deep reinforcement learning research field as a whole, after which you select - in groups of two - a specific deep RL paper of your interest. The paper may focus on i) a novel reinforcement learning algorithm, or ii) application of reinforcement learning to a specific problem area. The main goal of the course is to understand the paper and replicate its results. Throughout the course, you present your progress to the group for feedback, and you give feedback to others. If time permits, you may i) investigate your own novel extension of your paper or ii) try to improve reinforcement learning performance in your specific application area. At the end of the course, you write a report about your results, based on the guidelines of a true research paper.
Course objectives
After this course, the students are able to:
explain key topics in deep reinforcement learning research.
critically assess a deep reinforcement learning paper.
evaluate the experiment results of the replication of a deep reinforcement learning paper.
create a novel reinforcement learning algorithm or novel reinforcement learning application (optional).
give critical feedback on reinforcement learning research projects presented by peers.
present the progress of their reinforcement learning research project to peers.
write a paper about their reinforcement learning research project according to scientific standards.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lecture/seminar sessions, in which students present results to the class, and the teachers and class provide peer feedback.
Practicals, in which the students work on their research project, and teachers are available for advice.
Assessment method
The final grade is determined by:
Written paper: 80%.
Presentation of results to class: 10%
Peer feedback & class participation: 10%
To pass the course, the student needs to be present in at least 80% of the lectures. In case of a fail grade, there is an opportunity to retake the written exam, but only the same research project.
Reading list
The reading list changes per year and is available from the course website.
Registration
Due to limited capacity, you need to register for this course in advance. You register for the course by sending an email to the teacher before August 1. After confirmation of admittance to the course you can register for the course in MyStudyMap, see information below.
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: Thomas Moerland
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.