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

Teaching

For available theses topics, check out this page.

Courses

• Operations Research (SS21, SS22, SS23)

• Learning in Games Seminar (SS22, SS23)

• Seminar ITUB - "IT and Management Consulting" (WS 21/22, WS22/23)

• Auction Theory and Market Design (WS21/22, WS22/23)

Supervised Theses

Duc Tien Nguyen, Comparison of Deterministic and Distributional Reinforcement Learning Algorithms in Continous Auction Games, B.Sc. Informatics (2023)

Michal Cizevskij, A Comparison Between the Use of Unlabeled and Weakly Labeled Data in Active Learning for a Text Classification Problem, B.Sc. Informatics (2023) in cooperation with MaibornWolff

Ferdinand Brinker, Approximating Equilibrium Strategies in Sequential Colonel Blotto Games with Multi-Agent Reinforcement Learning, M.Sc. Informatics (2022)

Matija Novakovic, Analyzing Learning Dynamics in Finite N-Player Normal-Form Games with Varying Degrees of Cooperation, B.Sc. Information Systems (2022)

Felix Sittenauer, Imitation Learning for Robust Strategies in Multi-Agent Reinforcement Learning, M.Sc. Informatics (2022)

Julian Riß, Measuring Interdependencies in Multi-Agent Reinforcement Learning Systems in the Discounted Reward Setting, B.Sc. Informatics (2021)

Abyaad Rafid. Exploring Influence Structures in Multi-Agent Reinforcement Learning Systems, Guided Research B.Sc. Informatics (2021)

Korbinian Hainz. Learning Dynamics in Two-sided Matching Markets, B.Sc. Information Systems (2021)