Dr. Fabian Ritz

Fabian Ritz, Research Associate, LMU

Dr. Fabian Ritz, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Oettingenstraße 67
80538 München

Raum

Telefon:

Fax:

Mail: fabian.ritz@ifi.lmu.de

I left LMU at 2024-03-31 and won’t check this mail address regularly, conctacting via LinkedIn is preferred. Future publications won’t be listed here, have a look at my Google Scholar profile instead.

Teaching

Research Interests

  • Machine Learning
    • Adaptation of Software Engineering Processes and Techniques
  • Reinforcement Learning
    • Specification compliance
    • Safety
  • Multi-Agent Systems
    • Implicite and explicite coordination

Publications

2024

  • Michael Kölle, Mohamad Hgog, Fabian Ritz, Philipp Altmann, Maximilian Zorn and Claudia Linnhoff-Popien, „Quantum Advantage Actor-Critic for Reinforcement Learning“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART, 2024, pp.297-304.[DOI][preprint]
  • Michael Kölle, Yannick Erpelding, Fabian Ritz, Thomy Phan, Steffen Illium and Claudia Linnhoff-Popien, „Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART, 2024, pp.59-70. [DOI][code][preprint]

2023

  • Philipp Altmann, Fabian Ritz, Leonard Feuchtinger, Jonas Nüßlein, Claudia Linnhof-Popien, and Thomy Phan „CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing“, in 32nd International Joint Conference on Artificial Intelligence (IJCAI ’23), 2023, pp. 3414-3422. [DOI][preprint][source]
  • Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, and Claudia Linnhoff-Popien, „Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability“, in 40th International Conference on Machine Learning (ICML ’23), 2023.[DOI] [PDF][source]
  • Philipp Altmann, Thomy Phan, Fabian Ritz, Claudia Linnhoff-Popien, and Thomas Gabor „DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training“, in 15th Adaptive and Learning Agents Workshop (ALA), 2023. [PDF]
  • Thomy Phan, Fabian Ritz, Jonas Nüßlein, Michael Kölle, Thomas Gabor und Claudia Linnhoff-Popien. „Attention-Based Recurrence for Multi-Agent Reinforcement Learning under State Uncertainty“, Extended Abstracts of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2023, pp.2839–2841. [DOI][extended preprint][code]

2022

  • Fabian Ritz, Thomy Phan, Andreas Sedlmeier, Philipp Altmann, Jan Wieghardt, Reiner Schmid, Horst Sauer, Cornel Klein, Claudia Linnhoff-Popien and Thomas Gabor, „Capturing Dependencies within Machine Learning via a Formal Process Model“, in Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISOLA 2022, Lecture Notes in Computer Science, vol 13703, Springer International Publishing, pp.249–265. [DOI][PDF]
  • Fabian Ritz, Thomy Phan, Robert Müller, Thomas Gabor, Andreas Sedlmeier, Marc Zeller, Jan Wieghardt, Reiner Schmid, Horst Sauer, Cornel Klein and Claudia Linnhoff-Popien, „Specification Aware Multi-Agent Reinforcement Learning“, in Agents and Artificial Intelligence: ICAART, Lecture Notes in Computer Science, vol 13251, Springer International Publishing, 2022, pp. 3–21. [DOI][PDF]
  • Thomy Phan, Felix Sommer, Philipp Altmann, Fabian Ritz, Lenz Belzner, and Claudia Linnhoff-Popien, „Emergent Cooperation from Mutual Acknowledgment Exchange“, in 21th Conference on Autonomous Agents and Multiagent Systems (AAMAS ’22), 2022, pp. 1047–1055. [DOI][PDF][code]

2021

  • Thomy Phan, Fabian Ritz, Lenz Belzner, Philipp Altmann, Thomas Gabor, and Claudia Linnhoff-Popien, „VAST: Value Function Factorization with Variable Agent Sub-Teams“, in Advances in Neural Information Processing Systems (NeurIPS ’21), vol 34, Curran Associates, Inc., 2021, pp. 24018–24032. [PDF][code]
  • Fabian Ritz, Daniel Ratke, Thomy Phan, Lenz Belzner and Claudia Linnhoff-Popien, „A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement Learning“,  in Conference on Artificial Life (ALIFE’21), MIT Press, 2021, pp. 74–84. [DOI][PDF][code]
  • Fabian Ritz, Thomy Phan, Robert Müller, Thomas Gabor, Andreas Sedlmeier, Marc Zeller , Jan Wieghardt, Reiner Schmid, Horst Sauer, Cornel Klein and Claudia Linnhoff-Popien, „SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning“,  in 13th International Conference on Agents and Artificial Intelligence (ICAART’21), SciTePress, 2021, pp. 28–37. [DOI][PDF]
  • Robert Müller, Fabian Ritz, Steffen Illium and Claudia Linnhoff-Popien, „Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning“, in 13th International Conference on Agents and Artificial Intelligence (ICAART’21), SciTePress, 2021, pp. 49–56. [DOI][PDF]
  • Robert Müller, Steffen Illium, Fabian Ritz and Kyrill Schmid, „Analysis of Feature Representations for Anomalous Sound Detection“, in 13th International Conference on Agents and Artificial Intelligence (ICAART’21), SciTePress, 2021, pp. 97–106. [DOI][PDF]
  • Robert Müller, Steffen Illium, Fabian Ritz, Tobias Schröder, Christian Platschek, Jörg Ochs and Claudia Linnhoff-Popien, „Acoustic Leak Detection in Water Networks“, in 13th International Conference on Agents and Artificial Intelligence (ICAART’21), SciTePress, 2021, pp. 306–313. [DOI][PDF]
  • Thomy Phan, Lenz Belzner, Thomas Gabor, Andreas Sedlmeier, Fabian Ritz, and Claudia Linnhoff-Popien, „Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition“, in 35th AAAI Conference on Artificial Intelligence (AAAI ’21), 2021. [code][DOI][PDF]

2020

  • Fabian Ritz, Felix Hohnstein, Robert Müller, Thomy Phan, Thomas Gabor, Carsten Hahn and Claudia Linnhoff-Popien, „Towards Ecosystem Management from Greedy Reinforcement Learning in a Predator-Prey Setting“, in Conference on Artificial Life (ALIFE’20), MIT Press, 2020, pp. 518–525. [DOI][PDF]
  • Carsten Hahn, Fabian Ritz, Paula Wikidal, Thomy Phan, Thomas Gabor, and Claudia Linnhoff-Popien, „Foraging Swarms using Multi-Agent Reinforcement Learning“, in Conference on Artificial Life (ALIFE’20), MIT Press, 2020, pp. 333–340. [DOI][PDF]
  • Thomas Gabor, Leo Sünkel, Fabian Ritz, Thomy Phan, Lenz Belzner, Christoph Roch, Sebastian Feld, Claudia Linnhoff-Popien, „The Holy Grail of Quantum Artificial Intelligence: Challenges in Accelerating the Machine Learning Pipeline“, in First International Workshop on Quantum Software Engineering (Q-SE), 2020. [DOI][PDF]
  • Thomy Phan, Thomas Gabor, Andreas Sedlmeier, Fabian Ritz, Bernhard Kempter, Cornel Klein, Horst Sauer, Reiner Schmid, Jan Wieghardt, Marc Zeller, and Claudia Linnhoff-Popien, „Learning and Testing Resilience in Cooperative Multi-Agent Systems“, in 19th Conference on Autonomous Agents and Multiagent Systems (AAMAS ’20), 2020, pp. 1055–1063. [DOI][PDF][video]
  • Thomy Phan, Lenz Belzner, Kyrill Schmid, Thomas Gabor, Fabian Ritz, Sebastian Feld, and Claudia Linnhoff-Popien, „A Distributed Policy Iteration Scheme for Cooperative Multi-Agent Policy Approximation“, in Adaptive and Learning Agents Workshop (ALA@AAMAS ’20), 2020. [DOI][PDF][video]
  • Carsten Hahn, Thomy Phan, Sebastian Feld, Christoph Roch, Fabian Ritz, Andreas Sedlmeier, Thomas Gabor, and Claudia Linnhoff-Popien, „Nash Equilibria in Multi-Agent Swarms“, in 12th International Conference on Agents and Artificial Intelligence (ICAART’20), SciTePress, 2020, pp. 234–241. [DOI][PDF]
  • Thomas Gabor, Andreas Sedlmeier, Thomy Phan, Fabian Ritz, Marie Kiermeier, Lenz Belzner, Bernhard Kempter, Cornel Klein, Horst Sauer, Reiner Schmid, Jan Wieghardt, Marc Zeller and Claudia Linnhoff-Popien, „The scenario coevolution paradigm: adaptive quality assurance for adaptive systems“, in International Journal on Software Tools for Technology Transfer (2020) Vol. 22, Springer, 2020,  pp. 457–476.[DOI][PDF]

2019

  • Robert Müller, Stefan Langer, Fabian Ritz, Cristoph Roch, Steffen Illium, and Claudia Linnhoff-Popien, „Soccer Team Vectors“, in Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Springer, 2019, pp. 247–257. [DOI][PDF]
  • Daniel Elsner, Stefan Langer, Fabian Ritz, Robert Müller and Steffen Illium, „Deep Neural Baselines for Computational Paralinguistics“, in Proc. Interspeech 2019, ISCA Archive, 2019, pp. 2388–2392. [DOI][PDF]

2018

  • Fabian Ritz and Alf Zugenmaier, „The Impact of Uncle Rewards on Selfish Mining in Ethereum“, in 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), IEEE Computer Society, 2018, pp. 50–75. [DOI][PDF]

Supervised Theses

Master Theses

  • Ioan-Luca Ionescu, Maximilian Zorn, Faban Ritz, „Specification Aware Evolutionary Error Search in Parameterized RL Environments“, 2024
  • Clara Goldmann, Fabian Ritz, Maximilian Zorn, „Balancing Populations with Multi-Agent Reinforcement Learning“, 2024
  • Matthias Fruth, Fabian Ritz, Maximilian Zorn, „The Impact of Action Order in Multi-Agent Reinforcement learning“, 2023
  • Alexander Perzl, Thomy Phan, Fabian Ritz, „A Spatial Social Dilemma Environment for Multi-Agent Reinforcement Learning“, 2023
  • Julian Seiler, Steffen Illium, Fabian Ritz, „Untersuchung von Risikogewichtung durch Aktionswiederholung im Reinforcement Learning“, 2022
  • Nina Czogalla, Thomy Phan, Fabian Ritz, „Adaptive Resilient Multi-Agent Reinforcement Learning“, 2022
  • Leonhard Volk, Fabian Ritz, Robert Müller, „The Impact of Compensation in Potential Based Reward Shaping on Reinforcement Learning“, 2022
  • Manuel Zierl, Carsten Hahn, Thomy Phan, Fabian Ritz, „Analysis and Improvement of Communication in Cooperative Reinforcement Learning“, 2021
  • Marie Huttenloher, Fabian Ritz, Robert Müller, „Communication in Multi-Agent Reinforcement Learning with On – and Off-Policy Algorithms“, 2021
  • Filip Hristov, Fabian Ritz, Robert Müller, „Multi-Agent Reinforcement Learning with Communication in the SmartFactory Domain“, 2020
  • Max Peters, Thomy Phan, Fabian Ritz, „Assembly of Multi-Agent Formations using Reinforcement Learning“, 2020

Bachelor Theses

  • Yannick Erpelding, Michael Kölle, Fabian Ritz, „Development of a Universal Multi-Agent Reinforcement Learning Environment for Predator-Prey Research“, 2023
  • Barbara Böhm, Robert Müller, Fabian Ritz, „Coconut Palm Tree Counting in Drone Images with Deep Object Detection“, 2023
  • Mohamad Hgog, Michael Kölle, Fabian Ritz, „Quantum-Enhanced Policy Gradient Methods for Reinforcement Learning“, 2023
  • Dora Pruteanu, Andreas Sedlmeier, Fabian Ritz, „Analyse von Variational Bottlenecks hinsichtlich Problemstruktur und Lösungsqualität“, 2022
  • Paul Seipl, Thomy Phan, Fabian Ritz, „Evaluation of Aggregation Mechanisms for Federated Reinforcement Learning“, 2022
  • Weronika Maniszewska, Thomy Phan, Fabian Ritz, „Multi-Agent Reinforcement Learning with Transformer-Based Policies“, 2022
  • Caroline Reinig, Thomy Phan, Fabian Ritz, „Pretraining of Reinforcement Learning Models for Federated Learning“, 2021
  • Felix Hohnstein, Fabian Ritz, Robert Müller, „Resource Management through Reinforcement Learning in a Predator-Prey Scenario“, 2021
  • Louis Mackenzie-Smith, Fabian Ritz, Thomy Phan, „Creation of a Standard Challenge Dataset for Scalable Supervision in Reinforcement Learning“, 2021
  • Christian Reff, Fabian Ritz, Thomy Phan, „Adapting MCTS Planning Algorithms to a Continuous Domain“, 2021
  • Julius Rober, Fabian Ritz, Robert Müller, „Avoiding Negative Side Effects in Reinformcent Learning in a Gridworld“, 2021
  • Nikolai Limmbrunner, Fabian Ritz, Robert Müller, „Control Organisation of Autonomous Cabs for Smart Cities through Reinforcement Learning“, 2021
  • David Hansmair, Thomy Phan, Fabian Ritz, „Hexar.io as a Challenging Benchmark for Multi-Agent Reinforcement Learning“, 2020
  • Adrien Dutfoy, Fabian Ritz, Thomas Gabor, „Reinforcement Learning with Suboptimal Targets“, 2020
  • David Heine, Fabian Ritz, Carsten Hahn, „Analysis of Swarm Algorithms in Constrained Environments“, 2020
  • Sonja Geffert, Thomy Phan, Fabian Ritz, „Pooling von Target Networks im Deep Reinforcement Learning“, 2019
  • Lukas Urlasberger, Fabian Ritz, Robert Müller, „Count-based Exploration on State Transitions in Reinforcement Learning“, 2019