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Seminar Advanced Deep Reinforcement Learning


Admission requirements

Recommended prior knowledge

It is strongly recommended that students have successfully completed:
1. Reinforcement Learning
2. Introduction to Deep Learning
Students must be highly interested in scientific research and proficient in programming and machine learning and writing reports. This is an advanced course. We will work intensively on state of the art research topics.


Deep Reinforcement learning is a field of Artificial Intelligence that has attracted much attention, after history making achievements in Games and Robotics.
In this seminar course current scientific topics in recent Deep Reinforcement Learning research papers will be studied. Topics can include:

  • Transfer Learning

  • Meta Learning

  • Model-based Reinforcement Learning

  • Sample Efficiency

  • Hierarchical Reinforcement Learning
    In general the latest developments in the field are discussed.

General application areas are games, robotics, and other autonomous behavior. Typical benchmark applications of deep reinforcement learning are OpenAI Gym, DeepMind Bsuite, ALE, OpenSpiel, Meta World, PolyGames, and others.
Capacity is limited, attendance is mandatory, level of enthusiasm & difficulty is high. We will work in small groups studying recent scientific papers on the above topics.

This is a “third semester” course, after you have followed master courses on Reinforcement Learning and on Deep Learning.
Please see the Course Website

Course objectives

We study some of the latest research papers in advanced topics in deep reinforcement learning. Students learn about the latest research, by reading, understanding, implementing and presenting recent scientific insights in Deep Reinforcement Learning.
A paper will be chosen, the student will (1) reimplement (part of) the work, (2) present their work, and (3) write a paper about it.


The most recent timetable can be found at the Computer Science (MSc) student website.

You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

Student presentations about a research topic & implementation (with peer feedback) and papers (with feedback from lecturers). Together we study recent literature on topics in a common theme within the field of Deep Reinforcement Learning.

Course load

Hours of study: 168:00 hrs (= 6 EC)
Lectures: 26 hrs
At home preparation: 142 hrs

Assessment method

The final grade is determined by:

  • Presentations 10%

  • Active participation 20%

  • A written report/paper 100%

  • Peer review of a programming implementation 10%

The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.

Reading list

The reading list is changing every academic year according to the subject of study. See the course page of Brightspace for more information.


Register by 1) signing up in uSis and 2) sending an email to the lecturers before 1 August with your grades for Reinforcement Learning and Deep Learning.

Students with high grades for Reinforcement Learning and Deep Learning (Introduction to -, or - & Neural Networks) (unweighted average of the two courses) are invited by the lecturers for an intake meeting in August.

Every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.


Lecturer: Dr. Thomas Moerland and Prof.dr. Aske Plaat
Website: Brightspace
Course Website: here


Important: due to the format of the course, there is a limit on the number of participants: at most 12 students can participate in this course.

Detailed table of contents can be found in Brightspace.