263-5255-00L Foundations of Reinforcement Learning
| Semester | Spring Semester 2024 |
| Lecturers | N. He |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Abstract | Reinforcement learning (RL) has been in the limelight of many recent breakthroughs in artificial intelligence. This course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets M.S. students with strong research interests in reinforcement learning, optimization, and control. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course aims to provide students with an advanced introduction of RL theory and algorithms as well as bring them near the frontier of this active research field. By the end of the course, students will be able to - Identify the strengths and limitations of various reinforcement learning algorithms; - Formulate and solve sequential decision-making problems by applying relevant reinforcement learning tools; - Generalize or discover “new” applications, algorithms, or theories of reinforcement learning towards conducting independent research on the topic. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Topics include fundamentals of Markov decision processes, approximate dynamic programming, linear programming and primal-dual perspectives of RL, model-based and model-free RL, policy gradient and actor-critic algorithms, Markov games and multi-agent RL. If time allows, we will also discuss advanced topics such as batch RL, inverse RL, causal RL, etc. The course keeps strong emphasis on in-depth understanding of the mathematical modeling and theoretical properties of RL algorithms. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides will be posted on Moodle. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Dynamic Programming and Optimal Control, Vol I & II, Dimitris Bertsekas Reinforcement Learning: An Introduction, Second Edition, Richard Sutton and Andrew Barto. Algorithms for Reinforcement Learning, Csaba Czepesvári. Reinforcement Learning: Theory and Algorithms, Alekh Agarwal, Nan Jiang, Sham M. Kakade. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students are expected to have strong mathematical background in linear algebra, probability theory, optimization, and machine learning. | ||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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