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Lecturer
Clemens Heitzinger, Clemens.Heitzinger@TUWien.ac.at
Content and organization
Reinforcement learning (RL) is the field of artificial intelligence concerned with the development of strategies that an agent uses to maximize its rewards in a random environment, often without models. Applications include robotics, autonomous driving, (stochastic) optimal control, medicine, and games such as Go, chess, Atari 2600, StarCraft, Gran Turismo, and card games at human or superhuman level.
Chapters in this course include bandit problems, Markov decision problems, Bellman equations, dynamic programming, Monte-Carlo learning, temporal-difference learning, tabular methods, function approximation and deep RL, on-policy and off-policy learning, eligibility traces, policy gradients and actor-critic methods, deep RL, distributional RL, convergence, and PAC (probably approximately correct) estimates.
Lectures notes will be available for download.
Level
Postgraduate. Assumes knowledge of linear algebra, basic calculus and basic probability theory.
Course Duration
Two classes (1.5 hours each) per week.
Course Type
Semester Course
ECTS
6.
Marking Scheme
From 1 (excellent) to 5 (failed).
Participation terms
Registration at AIDA required.
Schedule
Tuesdays and Thursdays from 11:00 to 12:30 via Zoom.
Language
English.
Modality (online/in person):
Hybrid; online for remote participants. Zoom link will be available under the course link/homepage below.
Notes
There will be two tests. Participation in the tutorial part of the class is mandatory, where students present solutions of (theoretical and programming) exercises.
