Tobias Huber M.Sc.
| Phone: | (+49)(0)821 – 598 2336 |
| Email: | |
| Room: | 2015 (N) |
| Address: | Universitätsstraße 6a, 86159 Augsburg |
Links
Research interest
My research focuses on the explainability of Artificial Intelligence and Reinforcement Learning in particular.
I find it fascinating how Reinforcement Learning algorithms can independently develop strategies based only on observations and rewards. In some cases, the agents even develop new strategies that even humans have not yet considered (
e.g. by the chess computer AlphaZero). However, since only the goal of the agents is defined, it is often not clear what exactly the learned strategies look like. This is exacerbated by the use of modern machine learning techniques, which achieve considerable success but are also very opaque.
The goal of my research is to develop new algorithms that make the behaviour of intelligent agents explainable to users and thus facilitate the cooperation between humans and computers.
Talks
2021
Explainable deep Reinforcement Learning. Invited talk in the CSL Machine Learning Reading Club of the Computational Science Lab (CSL) at the University of Hohenheim, 02.02.2021, Slides .
Teaching
Courses
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WS20/21: Partical Course: Game Development
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WS 19/20: Partical Course: Game Development
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WS 18/19: Partical Course: Game Development
Insights into final projects
Supervised theses
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Using Reinforcement Learning to facilitate Implicit Learning in a VR Sports Simulation (Bachelor, 2021)
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Understanding Subliminal Persuasive Body Language in Political Speeches via ExplainableArtificial Intelligence (Bachelor, 2021)
- Verbinden von Belohnungs-Zerlegung und Strategie-Zusammenfassung für erklärbares Bestärkendes Lernen (Bachelor, 2021)
- Exploring an Explainable Reinforcement Learning Design for a Self Learning American Football Simulation (Master, 2020)
- Implementation and Comparison of Occlusion-based Explainable Artificial Intelligence Methods (Bachelor, 2020, Link)
- Implementierung und Vergleich verschiedener Salienz-Karten Algorithmen für tiefes bestärkendes Lernen (Master, 2019)