
Fabian Raoul Pieroth
E-Mail: fabian.pieroth at tum.de | |
Phone: +49 (0) 89 289 - 17532 | |
Office: Room 01.10.054 Boltzmannstr. 3 85748 Munich, Germany | |
Hours: by arrangement |
Short Bio
I am a Ph.D. student supervised by Prof. Bichler. My research focus lies in finding descriptive properties of Multi-Agent Systems, e.g., equilibrium-computation, or quantification of cooperation, through the use of multi-agent reinforcement learning.
Education:
- 10/2017 - 01/2021: Master in Mathematics (M. Sc.), Technical University Munich
- 10/2013 - 10/2016: Bachelor in Mathematics (B. Sc.), Ludwig-Maximilians-University Munich
Working Experience:
- 08/2018 - 05/2021: Data Science Intern, MaibornWolff
- 09/2017 - 08/2018: Data Science Intern, Horvath&Partners
- 10/2016 - 01/2017: Intern in Credit Risk and Methodology, Bayern LB
Publications
Journal Publications
Learning Equilibrium in Bilateral Bargaining Games
Martin Bichler, Nils Kohring, Matthias Oberlechner, Fabian R. Pieroth
European Journal of Operational Research (EJOR), Dec 2022
Peer-Reviewed Conference and Workshop Papers
Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets
Nils Kohring, Fabian R. Pieroth, Martin Bichler
Accepted at the Fortieth International Conference on Machine Learning (ICML), Jul 2023
Detecting Influence Structures in Multi-Agent Reinforcement Learning Systems
Fabian R. Pieroth, Katherine Fitch, Lenz Belzner
Accepted at the 2022 AAAI Workshop on Reinforcement Learning in Games (AAAI-RLG-22)
On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives
Xiyue Sun, Fabian R. Pieroth, Kyrill Schmid, Martin Wirsing, Lenz Belzner
Accepted at the 2022 DISCOLI Workshop on Distributed Collective Intelligence (DISCOLI 2022)
Working Papers
α-Rank-Collections: Analyzing Expected Strategic Behavior with Uncertain Utilities
Fabian R. Pieroth, Martin Bichler
Equilibrium Learning in Multi-Stage Games with Continuous Signal and Action Spaces
Fabian R. Pieroth, Nils Kohring, Martin Bichler
Academic Activities
- Program Committee Member, Uncertainty in Artificial Intelligence (UAI), 2021