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rudolph:cig_new [2021-10-27 15:29] Marco Pleines [Teaching] |
rudolph:cig_new [2021-10-27 19:05] Marco Pleines [Fachprojekt (technical project) Digital Entertainment Technologies] |
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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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* [[staff:nicolas_fischoeder|Nicolas Fischöder]] | * [[staff:nicolas_fischoeder|Nicolas Fischöder]] | ||
- | ===== Games for Computational Intelligence Research ===== | + | ===== Publications ===== |
+ | |||
+ | === Conference Articles (peer reviewed) === | ||
+ | |||
+ | == 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. | ||
+ | |||
+ | == 2019 == | ||
+ | |||
+ | * 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 == | ||
+ | |||
+ | * (WIP) Leon Swazinna. Evaluation of the MA-POCA Algorithm in a Competitive Reinforcement Learning Environment.\\ Advisors: Rudolph, Pleines. | ||
+ | |||
+ | == 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 == | ||
+ | |||
+ | * 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 == | ||
+ | |||
+ | * 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 === | ||
+ | |||
+ | == 2022 == | ||
+ | |||
+ | * (WIP) Marcel Schyma. Kontextunabhängige prozedurale Szenen- und Inhaltsgenerierung.\\ Advisors: Rudolph, Pleines. | ||
+ | |||
+ | == 2021 == | ||
+ | |||
+ | * Jonas Schumacher: Deep Reinforcement Learning für Stichspiele mit imperfekter Information / Deep Reinforcement Learning for Trick-Taking Games with Imperfect Information.\\ Advisors: Rudolph, Pleines. | ||
+ | |||
+ | |||
+ | |||
+ | ===== Teaching ===== | ||
+ | |||
+ | ==== Fachprojekt (technical project) Digital Entertainment Technologies ==== | ||
+ | |||
+ | * WiSe 2021/2022 | ||
+ | * Teacher: Patrick Dinklage | ||
+ | * SoSe 2021 | ||
+ | * Teacher: Patrick Dinklage | ||
+ | * WiSe 2020/2021 | ||
+ | * Teacher: Marco Pleines | ||
+ | * SoSe 2020 | ||
+ | * Teacher: Marco Pleines | ||
+ | * WiSe 2019/2020 | ||
+ | * Teacher: Marco Pleines | ||
+ | * [[https://ls11-www.cs.tu-dortmund.de/de/rudolph/lehre/fp_det_ss19|SoSe 2019]] | ||
+ | * Teacher: Marco Pleines | ||
+ | |||
+ | ==== Project Groups ==== | ||
+ | |||
+ | === PG 642: Verteiltes Deep Reinforcement Learning System zum Trainieren von Game AI === | ||
+ | |||
+ | The goal of this project group is to train agents to play Rocket League using a distributed Deep Reinforcement Learning system. | ||
+ | Training directly on Rocket League comes with many issues. Therefore, the game is reimplemented in Unity. | ||
+ | This raises the challenge of transferring the learned behavior in Unity to Rocket League, which is called a sim-to-sim transfer. | ||
+ | As this project is still ongoing, there are no outcomes to be presented yet. | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ===== Links ===== | ||
+ | |||
+ | ==== Games for Computational Intelligence Research ==== | ||
* [[https://github.com/Baekalfen/PyBoy|PyBoy]] | * [[https://github.com/Baekalfen/PyBoy|PyBoy]] | ||
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* [[https://github.com/Nordeus/heroic-rl|Heroic Magic Duel]] | * [[https://github.com/Nordeus/heroic-rl|Heroic Magic Duel]] | ||
- | |||
* [[https://github.com/MiscellaneousStuff/pylol|League of Legends]] | * [[https://github.com/MiscellaneousStuff/pylol|League of Legends]] | ||
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* [[https://sites.google.com/view/arena-unity/home/learning-environments|Arena]] | * [[https://sites.google.com/view/arena-unity/home/learning-environments|Arena]] | ||
- | ===== Publications ===== | ||
- | |||
- | === Journal Articles === | ||
- | |||
- | === Conference Articles (peer reviewed) === | ||
- | |||
- | === Technical Reports === | ||
- | |||
- | === Demonstration Articles === | ||
- | |||
- | ===== Theses ===== | ||
- | |||
- | === Master Theses === | ||
- | |||
- | === Bachelor Theses === | ||
- | |||
- | |||
- | |||
- | ===== Project Groups ===== | ||
- | |||
- | === PG 642: Verteiltes Deep Reinforcement Learning System zum Trainieren von Game AI === | ||
- | |||
- | The goal of this project group is to train agents to play Rocket League using a distributed Deep Reinforcement Learning system. | ||
- | Training directly on Rocket League comes with many issues. Therefore, the game is reimplemented in Unity. | ||
- | This raises the challenge of transferring the learned behavior in Unity to Rocket League, which is called a sim-to-sim transfer. | ||
- | As this project is still ongoing, there are no outcomes to be presented yet. | ||
- | |||
- | ===== Fachprojekte (technical projects) Digital Entertainment Technologies ===== | ||
- | |||
- | ===== Links ===== | ||