Christa Cuchiero: Catalogue data in Autumn Semester 2023 |  |
| Name | Prof. Dr. Christa Cuchiero |
| Address | Universität Wien Kolingasse 14-16 1090 Wien AUSTRIA |
| Telephone | 00431427738690 |
| cchrista@ethz.ch | |
| Department | Management, Technology, and Economics |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||
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| 365-1183-00L | Reinforcement Learning: Insights and Applications  Exclusively for MAS MTEC students (1st and 3rd semester). | 1 credit | 1S | C. Cuchiero, B. J. Bergmann, J. Teichmann | |||||||||||||||||||||||
| Abstract | Reinforcement learning (RL) is a field of machine learning that focuses on developing algorithms that enable an agent by novel machine learning technologies to learn optimal strategies through interaction with its environment. In this course we shall understand the main building blocks of (deep) RL and we shall discuss recent applications from finance and robotics. | ||||||||||||||||||||||||||
| Learning objective | After taking this course, students will - have an understanding of the fundamentals of reinforcement learning (RL), including the definition of an agent, environment, and rewards. - understand the idea of a Markov Decision Process, which is a mathematical framework used to model decision-making problems in RL. - understand the concept of a value function, which is used to measure the expected reward an agent can receive from a given state. - review various techniques for optimizing an agent's policy to maximize its expected reward - get an idea of Deep Reinforcement Learning: We will explore the use of deep neural networks in reinforcement learning and their advantages over traditional RL methods. - will understand the concept of Partially Observed Markov Decision Processes (POMDP) and its relation to MDPs. - gain hands-on experience with RL algorithms (optional) for MDPs and POMDPs. - see applications of DRL with a discussion of the real-world applications including finance and robotics. | ||||||||||||||||||||||||||
| Content | Reinforcement learning is a subfield of machine learning that focuses on developing algorithms that enable an agent to learn through trial and error by interacting with its environment. RL differs from other ML algorithms, e.g. supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The environment is typically stated in the form of a Markov decision process (MDP). In this course we will go through the main architecture of reinforcement learning and review some of its applications. On day 1 of the 2-day course the concept of a Markov Decision Process (MDP), its value function and the Bellmann equation are introduced and discussed. Several classical and ML powered algorithms are introduced and showcases presented. On Day 2 the concept of a partially observed Markov Decision Process is introduced. Aspects of Filtering and embedding partially observed Markov decision processes into the framework of MDPs are presented. Showcases from Robotics and Finance with an emphasis on the latter are presented in theory and applications. An understanding of basic machine learning concepts is welcomed but not mandatory (e.g. you took the class “Fundamentals on ML for Executives” or “AI for Executives”). In the beginning of the course, we will do a short primer on mathematics and statistics and some fundamental aspects of machine learning. We will provide coding examples for those you would like to follow the code. Grading (ungraded semester performance) is based on active participation in the class and a short written report (ungraded) after the course. | ||||||||||||||||||||||||||
Competencies |
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