2021
Final adaptation reinforcement learning for N-player games Journal Article
In: arXiv preprint arXiv:2111.14375, 2021.
2020
Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Inproceedings
In: Vasile, Massimiliano; Filipic, Bogdan (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA) , Bruxelles. Video (15 min) + slides available at https://youtu.be/OcpX7ITeH9w, 2020.
The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining), Oct 2020.
Final Adaptation Reinforcement Learning for N-Player Games Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.
2019
General Board Game Playing for Education and Research in Generic AI Game Learning Inproceedings
In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): IEEE Conference on Games, London, 2019.
The GBG Class Interface Tutorial V2.0: General Board Game Playing and Learning Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2019.
2017
The GBG Class Interface Tutorial: General Board Game Playing and Learning Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, 2017, (e-print published at http://www.gm.fh-koeln.de/ciopwebpub/Kone17a.d/TR-GBG.pdf).
2016
Online Adaptable Learning Rates for the Game Connect-4 Journal Article
In: IEEE Transactions on Computational Intelligence and AI in Games, vol. 8, no. 1, S. 33-42, 2016, (accepted 11/2014).
2015
Reinforcement Learning for Board Games: The Temporal Difference Algorithm Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 2015.
Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, 2015, (Updated version 2015).
2014
Connect-4 Game Playing Framework (C4GPF) Miscellaneous
2014.
Adaptation in Nonlinear Learning Models for Nonstationary Tasks Inproceedings
In: Bartz-Beielstein, T.; Filipic, B. (Hrsg.): PPSN'2014: 13th International Conference on Parallel Problem Solving From Nature, Ljubljana, S. 292–301, Springer, Heidelberg, 2014.
Online Adaptable Learning Rates for the Game Connect-4 Technical Report
CIplus no. TR 03/2014, 2014, (Preprint version of the article in IEEE Transactions on Computational Intelligence and AI in Games, 2016).
Temporal Difference Learning with Eligibility Traces for the Game Connect-4 Inproceedings
In: Preuss, Mike; Rudolph, Günther (Hrsg.): CIG'2014, International Conference on Computational Intelligence in Games, Dortmund, S. 84 – 91, 2014.
2012
Reinforcement learning with n-tuples on the game Connect-4 Inproceedings
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 184–194, Springer, Heidelberg, 2012.
2011
Self-configuration from a Machine-Learning Perspective Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 05/11; arXiv: 1105.1951, 2011, ISSN: 2191-365X, (e-print published at http://arxiv.org/abs/1105.1951 and Dagstuhl Preprint Archive, Workshop 11181 "Organic Computing -- Design of Self-Organizing Systems").
2009
Reinforcement learning for games: failures and successes -- CMA-ES and TDL in comparision Inproceedings
In: GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, S. 2641–2648, ACM, Montreal, Québec, Canada, 2009.
Evolutionsstrategien und Reinforcement Learning für strategische Brettspiele Technical Report
Cologne University of Applied Sciences 2009.
2008
Reinforcement Learning: Insights from Interesting Failures in Parameter Selection Inproceedings
In: Rudolph, Günter; others, (Hrsg.): PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund, S. 478–487, Springer, Berlin, 2008.
Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Technical Report
Cologne University of Applied Sciences 2008.
Reinforcement Learning für strategische Brettspiele Technical Report
Cologne University of Applied Sciences 2008.
Suchfeld
Reinforcement Learning für Standard Operating Procedures einer Atomkraftwerkssimulation Masters Thesis
TH Köln -- University of Applied Sciences, 2022.
Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath Masters Thesis
TH Köln -- University of Applied Sciences, 2022, (Bachelor thesis).
Reinforcement Learning in Simulationsspielen: Repräsentation von großen Aktions- und Zustandsräumen am Beispiel von Ökolopoly Masters Thesis
TH Köln -- University of Applied Sciences, 2022, (Bachelor thesis).
KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von Masters Thesis
TH Köln -- University of Applied Sciences, 2022, (Bachelor thesis).
Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework Technical Report
TH Köln -- University of Applied Sciences 2022, (Praxisprojekt).
TH Köln -- University of Applied Sciences, 2021, (Bachelor thesis).
Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Masters Thesis
TH Köln -- University of Applied Sciences, 2021, (Master thesis).
Erstellung eines Custom Environments in OpenAI Gym für das Spiel Ökolopoly Technical Report
TH Köln -- University of Applied Sciences 2021, (Praxisprojekt).
Final adaptation reinforcement learning for N-player games Journal Article
In: arXiv preprint arXiv:2111.14375, 2021.
AlphaZero-inspirierte KI-Agenten im General Board Game Playing Masters Thesis
TH Köln -- University of Applied Sciences, 2020, (Bachelor thesis).
