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rudolph:cig_new [2021-10-27 15:35] Marco Pleines [Publications] |
rudolph:cig_new [2021-10-27 15:43] 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 === | ||
| + | |||
| + | == 2021 == | ||
| + | |||
| + | * Alisa Gromova: Training Multiple Agents in a Soccer Environment using Deep Reinforcement Learning.\\ Advisors: Rudolph, Pleines. | ||
| + | |||
| + | Learning and Self-Play | ||
| == 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. |
| === Master Theses === | === Master Theses === | ||
| - | === Bachelor Theses === | + | == 2021 == |
| + | |||
| + | * Jonas Schumacher: Deep Reinforcement Learning für Stichspiele mit imperfekter Information / Deep Reinforcement Learning for Trick-Taking Games with Imperfect Information.\\ Advisors: Rudolph, Pleines. | ||