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rudolph:cig_new [2021-10-27 15:35] Marco Pleines [Publications] |
rudolph:cig_new [2021-10-27 15:45] Marco Pleines [Theses (Abschlussarbeiten)] |
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===== Research Context ===== | ===== Research Context ===== | ||
- | * Genetic and Evolutionary Algortihms | + | * Deep Reinforcement Learning |
- | * Reinforcement Learning | + | |
* Procedural Content Generation | * Procedural Content Generation | ||
* Generative Models | * Generative Models | ||
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===== Publications ===== | ===== Publications ===== | ||
+ | === Conference Articles (peer reviewed) === | ||
+ | == 2020 == | ||
- | === Journal Articles === | + | * Marco Pleines, Jenia Jitsev, Mike Preuss, Frank Zimmer. [[https://arxiv.org/abs/2004.00567|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 == | ||
- | === Conference Articles (peer reviewed) === | + | * Marco Pleines, Frank Zimmer, Vincent-Pierre Berges. [[http://www.ieee-cog.org/papers/paper_22.pdf|Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices]]. In CoG 2019 Proceedings, IEEE. |
+ | |||
+ | === Competitions === | ||
+ | |||
+ | == 2019 == | ||
+ | |||
+ | * Marco Pleines, Mike Preuss, Jenia Jitsev, Frank Zimmer, Jonathan Indetzki. [[https://youtu.be/P2rBDHBHxcM|Rising to the Obstacle Tower Challenge]]. In CoG 2019 Short Video Competition, IEEE | ||
+ | ===== Theses (Abschlussarbeiten) ===== | ||
+ | |||
+ | === Bachelor Theses === | ||
+ | |||
+ | == 2022 == | ||
+ | |||
+ | == 2021 == | ||
+ | |||
+ | * Alisa Gromova: Training Multiple Agents in a Soccer Environment using Deep Reinforcement Learning and Self-Play.\\ Advisors: Rudolph, Pleines. | ||
+ | * Markus Grigull: Sim-to-Real Transfer eines Reinforcement Learning Ansatzes zur mechanischen Steuerung eines Gamepads.\\ Advisors: Rudolph, Pleines. | ||
== 2020 == | == 2020 == | ||
- | * Marco Pleines, Jenia Jitsev, Mike Preuss, Frank Zimmer. [[https://arxiv.org/abs/2004.00567|Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning]]. In CoG 2020 Proceedings, IEEE. Best Paper Candidate. | + | * Matthias Pallasch. Curiosity-driven Exploration mit Reinforcement Learning in einer CoinRun Umwelt.\\ Advisors: Rudolph, Pleines. |
+ | * Vanessa Speeth. Entwicklung eines Agenten für das Spiel Azul basierend auf dem Advanced-Actor-Critc Ansatz.\\ Advisors: Rudolph, Pleines. | ||
+ | * Wentao Li. Applying Curriculum and Reinforcement Learning to a Marble Labyrinth Environment.\\ Advisors: Rudolph, Pleines. | ||
+ | == 2019 == | ||
- | ===== Theses (Abschlussarbeiten) ===== | + | * Till Musshoff. Vergleich der Lersperformanz von Proximal Policy Optimization und Behavioral Cloning.\\ Advisors: Rudolph, Pleines. |
+ | * Marius Brinkmann. Evaluation der Reinforcement Learning-Algorithmen DQN und PPO in einer Ballwurf-Umwelt.\\ Advisors: Rudolph, Pleines. | ||
=== Master Theses === | === Master Theses === | ||
- | === Bachelor Theses === | + | == 2022 == |
+ | |||
+ | == 2021 == | ||
+ | |||
+ | * Jonas Schumacher: Deep Reinforcement Learning für Stichspiele mit imperfekter Information / Deep Reinforcement Learning for Trick-Taking Games with Imperfect Information.\\ Advisors: Rudolph, Pleines. | ||