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ModsMH4521

Reinforcement Learning

Current offering — AY2026/2027 Semester 1

This course will introduce the framework of reinforcement learning including some theoretical aspects and practical algorithms. The course will start with special cases such as multi-armed bandits before moving on to Markov decision processes and the corresponding planning and online reinforcement learning problems.

If you want to build a solid understanding of the principles and fundamental results backing reinforcement learning, as well as develop some intuition about the methodology and practical challenges of this approach, then this course is meant for you.

This course will equip you with a practical understanding of reinforcement learning, hence allowing you to apply this type of methods in machine-learning-related jobs. The theoretical insights gained in this course will also help you adapt to future developments in the field.

Total hours per week: 6 hrs

AUs4.0 AUs
Grade Type
PrerequisiteMH2500, MH3512
Exam24 November 2026, 5.00 pm - 7.00 pm

The Exam information shown may be subject to changes. Students are to check the finalised exam timetable with exam seat information, which will be available at the 'Examination Seating Arrangement' webpage, 2 weeks before start of examination.


Prerequisite Graph

MH4521

Reinforcement Learning

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Available Indexes

MonTueWedThuFri
930

70327 LAB (LA)

0930-1120 Tue

COMP LAB 3

COMMON LEC (LE)

0930-1120 Wed

SPMS-LT5

1000
1030
1100
1130
1200
1230
1300
1330
1400
1430
1500
1530
1600
1630

COMMON LEC (LE)

1630-1720 Thu

SPMS-LT5

1700
1730

70327 TUT (T)

1730-1820 Thu

SPMS-LT5

1800

Other offerings

AY25/26
Semester 1Semester 2Sp. Term
AY24/25
Semester 1Semester 2Sp. Term

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