Marco Pleines
Curriculum Vitae
01/2019 - today TU Dortmund University, Computer Science PhD, Dortmund
09/2016 - 12/2018 Rhine-Waal University of Applied Sciences, M.Sc. Information Engineering and Computer Science, Kamp-Lintfort
09/2012 - 08/2016 Rhine-Waal University of Applied Sciences, B.Sc. Media and Communication Computer Science, Kamp-Lintfort
08/2008 - 05/2009 Jan-Joest-Gymnasium der Stadt Kalkar, Abitur (equivalent to A-Levels in Britain), Kalkar
08/2008 - 05/2009 Northview High School, Foreign Exchange Student, Grand Rapids, Michigan, USA
01/2019 - today Scientific Associate, Department of Computer Science, TU Dortmund University
01/2017 - 12/2018 Scientific Associate, Rhine-Waal University of Applied Sciences, Kamp-Lintfort
08/2014 - 09/2014 Praktikant, BInteractive, Porto
02/2013 - 12/2016 Student Assistant, Rhine-Waal University of Applied Sciences Kamp-Lintfort
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Summer Semster 2022
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Projektgruppe 649: Entwicklung eines 3D RPG Videospiels mittels prozeduraler Inhaltsgenerieung und Deep Reinforcement Learning
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Winter Semester 2021/2022
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Advanced AI Applications (Rhine-Waal University of Applied Sciences)
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Rainbow DQN, Proximal Policy Optimization
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Ausgewählte Kapitel der Computational Intelligence (zeitweise Vertretung)
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Rainbow DQN, Proximal Policy Optimization
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Projektgruppe 642: Verteiltes Deep Reinforcement Learning System zum Trainieren von Game AI
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Sim-to-sim Transfer angewandt auf Rocket League
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Summer Semester 2021
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Projektgruppe 642: Verteiltes Deep Reinforcement Learning System zum Trainieren von Game AI
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Sim-to-sim Transfer angewandt auf Rocket League
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Winter Semester 2020/2021
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Fachprojekt: Digital Entertainment Technologies
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Summer Semester 2020
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Fachprojekt: Digital Entertainment Technologies
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Winter Semester 2019/2020
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Fachprojekt: Digital Entertainment Technologies
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Summer Semester 2019
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Fachprojekt: Digital Entertainment Technologies
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2022
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M.Sc. Marcel Schyma. Kontextunabhängige prozedurale Szenen- und Inhaltsgenerierung.
Advisor: Rudolph, Pleines. -
B.Sc. Leon Swazinna. Evaluation of the MA-POCA Algorithm in a Competitive Reinforcement Learning Environment.
Advisor: Rudolph, Pleines.
2021
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M.Sc. Jonas Schumacher. Deep Reinforcement Learning für Stichspiele mit imperfekter Information / Deep Reinforcement Learning for Trick-Taking Games with Imperfect Information.
Advisor: Rudolph, Pleines. -
B.Sc. Alisa Gromova. Training Multiple Agents in a Soccer Environment using Deep Reinforcement Learning and Self-Play.
Advisor: Rudolph, Pleines. -
B.Sc. Markus Grigull. Sim-to-Real Transfer eines Reinforcement Learning Ansatzes zur mechanischen Steuerung eines Gamepads.
Advisor: Rudolph, Pleines.
2020
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B.Sc. Matthias Pallasch. Curiosity-driven Exploration mit Reinforcement Learning in einer CoinRun Umwelt.
Advisor: Rudolph, Pleines. -
B.Sc. Vanessa Speeth. Entwicklung eines Agenten für das Spiel Azul basierend auf dem Advanced-Actor-Critc Ansatz.
Advisor: Rudolph, Pleines. -
B.Sc. Wentao Li. Applying Curriculum and Reinforcement Learning to a Marble Labyrinth Environment.
Advisor: Rudolph, Pleines.
2019
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B.Sc. Till Musshoff. Vergleich der Lersperformanz von Proximal Policy Optimization und Behavioral Cloning.
Advisor: Rudolph, Pleines. -
B.Sc. Marius Brinkmann. Evaluation der Reinforcement Learning-Algorithmen DQN und PPO in einer Ballwurf-Umwelt.
Advisor: Rudolph, Pleines.
Further Information
Consulting Hours: By arrangement
Publications
Conference Articles (peer reviewed)
2022
- Marco Pleines, Konstantin Ramthun, Yannik Wegener, Hendrik Meyer, Matthias Pallasch, Sebastian Prior, Jannik Drögemüller, Leon Büttinghaus, Thilo Röthemeyer, Alexander Kaschwig, Oliver Chmurzynski, Frederik Rohkrämer, Roman Kalkreuth, Frank Zimmer, Mike Preuss. On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer. Accepted at CoG 2022, IEEE
- Jonas Schumacher, Marco Pleines. Improving Bidding and Playing Strategies in the Trick-Taking game Wizard using Deep Q-Networks. Accepted at CoG 2022, IEEE.
2020
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Marco Pleines, Jenia Jitsev, Mike Preuss, Frank Zimmer. Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning. In CoG 2020 Proceedings, IEEE. Best Paper Candidate.
2019
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Marco Pleines, Frank Zimmer, Vincent-Pierre Berges. Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices. In CoG 2019 Proceedings, IEEE.
Preprints
2022
- Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss. Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization. Preprint.
Book Chapters
2020
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Marco Pleines, Frank Zimmer, Jonathan Indetzki, Fabian Fritzsche, Timo Kahl. Reinforcement Learning auf dem Weg in die Industrie. In Digitale Produktion - Nutzenversprechen, Lösungsansätze, Soziale Fragen. Torsten Niechoj (Ed.) & Alexander Klein (Ed.). Metropolis-Verlag.
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Marco Pleines. Generative Adversarial Networks. In Interaktive Datenvisualisierung in Wissenschaft und Unternehmenspraxis. Frank Zimmer (Ed.) & Timo Kahl (Ed.). Springer-Verlag.
Competitions
2019
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Marco Pleines, Mike Preuss, Jenia Jitsev, Frank Zimmer, Jonathan Indetzki. Rising to the Obstacle Tower Challenge. In CoG 2019 Short Video Competition, IEEE