Reinforcement Learning for Understanding and Modeling Human Behavior 2025
Reinforcement learning (RL) methods have advanced, and it has the potential to offer robust policies for modeling users and guide adaptive systems. Those advancements lead to many open challenges and wide application scope. In this course, students present and discuss papers from relevant top-tier research venues to extract techniques and insights from RL research and application in HCI. In this course, students present and discuss papers from relevant top-tier research venues to extract techniques and insights from RL research and application in Human-Computer Interaction. The objective of the seminar is for participants to collectively learn about the state-of-the-art research in Reinforcement Learning and closely related areas. This includes the ability to concisely present results of pioneering as well as state-of-the-art research. Another objective is to collectively discuss open issues in the field and developing a feeling for what constitutes research questions and outcomes in the field of technical Human-Computer Interaction.
Overview
- Seminar
- 263-5910-00L Reinforcement Learning for Understanding and Modeling Human Behavior
- Lecturers
- Christian Holz and Yi-Chi Liao
- Communication
- Please address all questions (on content, organization, etc.) on Slack
link to Reinforcement Learning for Understanding and Modeling Human Behavior Slack channel 2025 - Lecture
-
ETH lecture room CAB D 46.
Wednesdays, 4pm–6pm
first seminar: 19.02.2025
last seminar: 28.05.2025 - Credits
- 2 ECTS
- Materials
- slides, assignments, and recordings