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CI in Games
Solving Problems in Games
by Means of
Computational Intelligence
Research Context
- Deep Reinforcement Learning
- Procedural Content Generation
- Generative Models
- Deep Learning
Contacts
Publications
Conference Articles (peer reviewed)
2020
- 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
- Marco Pleines, Frank Zimmer, Vincent-Pierre Berges. 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. 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
2019
Master 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.
2020
2019
Teaching
Fachprojekt (technical project) Digital Entertainment Technologies
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.