Admission requirements
No formal requirements.
The course builds on concepts from the bachelor courses Symbolic AI and Machine Learning. Students are expected to program in Python.
Description
For detailed information please visit the course website.
Reinforcement learning is a part of machine learning that focuses on agents interacting in an environment. The goal is to learn a sequence of actions that maximizes the rewards obtained by the agent. The field is rapidly growing, with a wide range of applications in games, robotics, and general decision-making. This course provides a broad introduction to the fundamental (tabular) concepts of Reinforcement Learning. A large part of the course focuses on acquiring hands-on practical experience with applying reinforcement learning algorithms.
Course objectives
After this course, the students are able to:
identify the position of reinforcement learning within the broader machine learning field, and recognize real-world problems suitable for reinforcement learning.
explain the fundamental concepts of sequential decision making, including Markov Decision Processes and Dynamic Programming.
describe the main challenges that appear in reinforcement learning, such as the exploration/exploitation trade-off and the credit assignment problem, and possible solution approaches.
compare different reinforcement learning algorithms, such as model-free versus model-based or on-policy versus off-policy approaches, on their strengths and weaknesses.
implement reinforcement learning algorithms in Python and test them on different environments.
analyze the outcome of reinforcement learning experiments and identify possible explanations.
Timetable
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
Lectures.
Practicals, in which the students can work on the practical assignments, and teachers are available for advice.
Assessment method
Written examination with closed questions (50%)
Assignments (50%)
The final grade for the course is established by determining the weighted average. However, both partial grades need to be above the passing margin. There is an opportunity to retake the exam.The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
Sutton & Barto, Reinforcement Learning: An Introduction, 2nd edition. This is the main textbook of the course, and it has a free online pdf version.
Plaat, Learning to Play, 1st edition. This is supplementary material, and also comes with a free online pdf version.
Course reader
Registration
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.