Connect-4 Game Playing Framework (C4GPF).png "Connect-4 board. Red to move. Red is under zugzwang and will loose the game. The numbers indicate the states of some so-called \"n-tuples\".")
Download C4GPF from GitHub
Connect-4 (Connect-Four) is an interesting game for several reasons. It is a non-trivial game, most humans will have difficulties to play well against a master level player (human or computer). At the same time it is of medium complexity (4.5·1012 states). It can be solved by tree-based methods (minimax, alpha-beta pruning).
Over the last years we have worked on a Java-based Connect-4 framework which makes it easily to develop, test and play with trainable Connect-4 agents. This work is now ready to be shared with other interested researchers or game-playing users. You can download it for your own research from this GitHub link.
"Why another Connect-4 program?", you might ask. There are many Connect-4 realizations, but – at least to our knowledge – no other one which incorporates a fast-playing perfect player. This player can be used to benchmark the strength of other, self-trained agents. We derived a variety of trainable agents (more details in our recent TCIAIG-paper "Online Adaptable Learning Rates for the Game Connect-4"). We have developed the framework in such a way that it is for an experienced Java programmer fairly easy to add his / her own agent. Once added, several methods for evaluation are at the disposal of the user.
So we want to encourage with this Connect-4 framework other researchers to benchmark their game-playing agents against other Connect-4 agents with well-known strength. If you are interested in having your Connect-4 agent being added to the C4GPF framework, feel free to contact us via eMail.
Features of C4GPF:
- built-in reinforcement learning agent (TD-learning)
- eligibility traces
- several adaptive step-size learning schemes: TCL, IDBD, …
- N-tuple features
- perfect-playing Minimax agent with alpha-beta pruning and opening book
- interface "Agent.java" for easy plug-in of new agents
- several benchmarking options (competitions, move inspections, …)
Getting started:
- Read the file CFour/READM.txt on GitHub
- Read the file CFour/src/doc/index.htm = CFour/src/doc/Help.pdf (help file for the GUI of C4GPF)
Authors of C4GPF:
- Markus Thill (markus.thill "at" fh-koeln.de)
- Wolfgang Konen (wolfgang.konen "at" fh-koeln.de)
Download C4GPF from GitHub
Publications Games
2024
Albers, Alexander
Der Einfluss einer intuitiveren graphischen UI und weiteren spielunterstützenden Features auf den Spielerfolg und Spielspaß im Spiel Ökolopoly Masters Thesis
TH Köln – University of Applied Sciences, 2024, (Bachelor thesis).
@mastersthesis{Albers2024,
title = {Der Einfluss einer intuitiveren graphischen UI und weiteren spielunterstützenden Features auf den Spielerfolg und Spielspaß im Spiel Ökolopoly },
author = {Alexander Albers},
year = {2024},
date = {2024-12-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Marcus, Tobias
Untersuchung von stochastischen und nicht-stochastischen Reinforcement-Learning-Algorithmen für Blackjack und Kuhn Poker Masters Thesis
TH Köln – University of Applied Sciences, 2024, (Bachelor thesis).
@mastersthesis{Marcus2024,
title = {Untersuchung von stochastischen und nicht-stochastischen Reinforcement-Learning-Algorithmen für Blackjack und Kuhn Poker },
author = {Tobias Marcus},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-TMarcus2024-final.pdf},
year = {2024},
date = {2024-07-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
2023
Konen, Wolfgang
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning Journal Article
In: arXiv preprint arXiv:2301.12167, 2023.
@article{Konen2023b,
title = {Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning},
author = {Wolfgang Konen},
url = {https://arxiv.org/abs/2301.12167},
year = {2023},
date = {2023-01-01},
journal = {arXiv preprint arXiv:2301.12167},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Seven, Meltem
KI-Agenten im Vergleich: Erfolg von Agenten des Ludii General Game Systems in Partien gegen perfekte oder starke Agenten am Beispiel der Spiele Vier Gewinnt, Nim und Othello Masters Thesis
TH Köln – University of Applied Sciences, 2023, (Bachelor thesis).
@mastersthesis{Seven2023,
title = {KI-Agenten im Vergleich: Erfolg von Agenten des Ludii General Game Systems in Partien gegen perfekte oder starke Agenten am Beispiel der Spiele Vier Gewinnt, Nim und Othello},
author = {Meltem Seven},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Meltem-Seven-final.pdf},
year = {2023},
date = {2023-01-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
2022
Konen, Wolfgang
The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning Technical Report
TH Köln 2022.
@techreport{Konen2022a,
title = {The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning},
author = {Wolfgang Konen},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Konen22a.d/TR-GBG.pdf},
year = {2022},
date = {2022-09-01},
institution = {TH Köln},
school = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Fabig, Niklas
Reinforcement Learning für Standard Operating Procedures einer Atomkraftwerkssimulation Masters Thesis
TH Köln -- University of Applied Sciences, 2022, (Master thesis).
@mastersthesis{Fabig2022,
title = {Reinforcement Learning für Standard Operating Procedures einer Atomkraftwerkssimulation},
author = {Niklas Fabig},
year = {2022},
date = {2022-08-01},
urldate = {2022-08-01},
school = {TH Köln -- University of Applied Sciences},
note = {Master thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Weitz, Ann
Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath Masters Thesis
TH Köln -- University of Applied Sciences, 2022, (Bachelor thesis).
@mastersthesis{Weitz2022b,
title = {Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath},
author = {Ann Weitz},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Weitz-final-2022.pdf},
year = {2022},
date = {2022-05-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Raycheva, Ralitsa
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).
@mastersthesis{Raycheva2022,
title = {Reinforcement Learning in Simulationsspielen: Repräsentation von großen Aktions- und Zustandsräumen am Beispiel von Ökolopoly},
author = {Ralitsa Raycheva},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Raycheva2022-final.pdf},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Cöln, Julian
KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!" Masters Thesis
TH Köln -- University of Applied Sciences, 2022, (Bachelor thesis).
@mastersthesis{Cöln2022,
title = {KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!"},
author = {Julian Cöln},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Julian_Coeln2021-final.pdf},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Weitz, Ann
Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework Technical Report
TH Köln -- University of Applied Sciences 2022, (Praxisprojekt).
@techreport{Weitz2022,
title = {Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework},
author = {Ann Weitz},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/PP-Doku-Weitz-2022-02.pdf},
year = {2022},
date = {2022-02-01},
institution = {TH Köln -- University of Applied Sciences},
note = {Praxisprojekt},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Scheiermann, Johannes; Konen, Wolfgang
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Journal Article
In: IEEE Transactions on Games, 2022.
@article{Scheier2022,
title = {AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time},
author = {Johannes Scheiermann and Wolfgang Konen},
url = {https://ieeexplore.ieee.org/document/9893320},
doi = {10.1109/TG.2022.3206733},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Games},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Scheiermann, Johannes; Konen, Wolfgang
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Journal Article
In: arXiv preprint arXiv:2204.13307, 2022, (Preprint of the IEEE ToG 2022 paper).
@article{Scheier2022arXiv,
title = {AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time},
author = {Johannes Scheiermann and Wolfgang Konen},
url = {https://arxiv.org/abs/2204.13307},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2204.13307},
note = {Preprint of the IEEE ToG 2022 paper},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Meissner, Simon
Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG) Masters Thesis
TH Köln -- University of Applied Sciences, 2021, (Bachelor thesis).
@mastersthesis{Meissner2021,
title = {Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG)},
author = {Simon Meissner},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Meissner-final-2021.pdf},
year = {2021},
date = {2021-12-01},
urldate = {2021-12-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Zeh, Tim
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).
@mastersthesis{Zeh2021,
title = {Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework},
author = {Tim Zeh},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_Zeh_final_Poker-GBG-2021.pdf},
year = {2021},
date = {2021-01-01},
school = {TH Köln -- University of Applied Sciences},
note = {Master thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Raycheva, Ralitsa
Erstellung eines Custom Environments in OpenAI Gym für das Spiel Ökolopoly Technical Report
TH Köln -- University of Applied Sciences 2021, (Praxisprojekt).
@techreport{Raycheva2021,
title = {Erstellung eines Custom Environments in OpenAI Gym für das Spiel Ökolopoly},
author = {Ralitsa Raycheva},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/PP_Oekolopoly_Raycheva_final.pdf},
year = {2021},
date = {2021-01-01},
institution = {TH Köln -- University of Applied Sciences},
note = {Praxisprojekt},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang; Bagheri, Samineh
Final adaptation reinforcement learning for N-player games Journal Article
In: arXiv preprint arXiv:2111.14375, 2021.
@article{konen2021final,
title = {Final adaptation reinforcement learning for N-player games},
author = {Wolfgang Konen and Samineh Bagheri},
url = {https://arxiv.org/abs/2111.14375},
year = {2021},
date = {2021-01-01},
journal = {arXiv preprint arXiv:2111.14375},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Scheiermann, Johannes
AlphaZero-inspirierte KI-Agenten im General Board Game Playing Masters Thesis
TH Köln -- University of Applied Sciences, 2020, (Bachelor thesis).
@mastersthesis{Scheier2020b,
title = {AlphaZero-inspirierte KI-Agenten im General Board Game Playing},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Scheiermann_final.pdf},
year = {2020},
date = {2020-12-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Konen, Wolfgang; Bagheri, Samineh
Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Proceedings Article
In: Vasile, Massimiliano; Filipic, Bogdan (Ed.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA)
, Bruxelles. Video (15 min) + slides available at https://youtu.be/OcpX7ITeH9w, 2020.
@inproceedings{Konen20b,
title = {Reinforcement Learning for N-Player Games: The Importance of Final Adaptation},
author = {Wolfgang Konen and Samineh Bagheri},
editor = {Massimiliano Vasile and Bogdan Filipic },
url = {https://www.gm.fh-koeln.de/ciopwebpub/Konen20b.d/bioma20-TDNTuple.pdf},
year = {2020},
date = {2020-10-01},
urldate = {2020-10-01},
booktitle = {9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA)
},
address = {Bruxelles. Video (15 min) + slides available at https://youtu.be/OcpX7ITeH9w},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Konen, Wolfgang
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.
@techreport{Konen20d,
title = {The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning},
author = {Wolfgang Konen},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Konen20d.d/TR-GBG.pdf},
year = {2020},
date = {2020-01-10},
urldate = {2020-01-10},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining), Oct},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang; Bagheri, Samineh
Final Adaptation Reinforcement Learning for N-Player Games Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.
@techreport{Konen20b_TR,
title = {Final Adaptation Reinforcement Learning for N-Player Games},
author = {Wolfgang Konen and Samineh Bagheri},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Konen20b_TR.d/Konen20b_TR.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Scheiermann, Johannes
Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Technical Report
TH Köln, Institut für Informatik 2020, (Praxisprojekt).
@techreport{Scheier2020,
title = {Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten?},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Prj-Scheiermann-2020-08.pdf},
year = {2020},
date = {2020-01-01},
institution = {TH Köln, Institut für Informatik},
note = {Praxisprojekt},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
2019
Konen, Wolfgang
General Board Game Playing for Education and Research in Generic AI Game Learning Proceedings Article
In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Ed.): IEEE Conference on Games, London, 2019.
@inproceedings{Konen19b,
title = {General Board Game Playing for Education and Research in Generic AI Game Learning},
author = {Wolfgang Konen},
editor = {Diego Perez and Sanaz Mostaghim and Simon Lucas},
url = {https://arxiv.org/pdf/1907.06508},
year = {2019},
date = {2019-08-20},
booktitle = {IEEE Conference on Games},
address = {London},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Konen, Wolfgang
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.
@techreport{Konen19a,
title = {The GBG Class Interface Tutorial V2.0: General Board Game Playing and Learning},
author = {Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone19a.d/TR-GBG.pdf},
year = {2019},
date = {2019-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Barsnick, Felix
Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen Masters Thesis
TH Köln -- University of Applied Sciences, 2019, (Master thesis).
@mastersthesis{Barsnick2019,
title = {Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen},
author = {Felix Barsnick},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_MMI_Barsnick-2019-04-final.pdf},
year = {2019},
date = {2019-01-01},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Master thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Cöln, Julian; Dittmar, Yannick
Untersuchung von KI Agenten im Spiel Othello Technical Report
TH Köln, Institut für Informatik 2019.
@techreport{Cöln2019,
title = {Untersuchung von KI Agenten im Spiel Othello},
author = {Julian Cöln and Yannick Dittmar},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Proj-DittmarCoeln-2019-12.pdf},
year = {2019},
date = {2019-01-01},
institution = {TH Köln, Institut für Informatik},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
2017
Galitzki, Kevin
Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework Masters Thesis
TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis).
@mastersthesis{Galitzki2017,
title = {Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework},
author = {Kevin Galitzki},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-KevinGalitzki-final-2017.pdf},
year = {2017},
date = {2017-09-15},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Konen, Wolfgang
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).
@techreport{Kone17a,
title = {The GBG Class Interface Tutorial: General Board Game Playing and Learning},
author = {Konen, Wolfgang},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone17a.d/TR-GBG.pdf},
year = {2017},
date = {2017-06-01},
address = {Cologne University of Applied Science},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
note = {e-print published at http://www.gm.fh-koeln.de/ciopwebpub/Kone17a.d/TR-GBG.pdf},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Kutsch, Johannes
KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele Masters Thesis
TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis, 2nd prize in CBC award 2018).
@mastersthesis{Kutsch2017b,
title = {KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele},
author = {Johannes Kutsch},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_JohannesKutsch_Final-2017.pdf},
year = {2017},
date = {2017-01-01},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis, 2nd prize in CBC award 2018},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
2016
Bagheri, Samineh; Thill, Markus; Koch, Patrick; Konen, Wolfgang
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, pp. 33-42, 2016, (accepted 11/2014).
@article{Bagh16b,
title = {Online Adaptable Learning Rates for the Game Connect-4},
author = { Samineh Bagheri and Markus Thill and Patrick Koch and Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen//Bagh15.pdf},
doi = {10.1109/TCIAIG.2014.2367105},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
volume = {8},
number = {1},
pages = {33-42},
note = {accepted 11/2014},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2015
Konen, Wolfgang
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.
@techreport{Kone15c,
title = {Reinforcement Learning for Board Games: The Temporal Difference Algorithm},
author = { Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone15c.d/TR-TDgame_EN.pdf},
doi = {10.13140/RG.2.1.1965.2329},
year = {2015},
date = {2015-07-01},
address = {Cologne University of Applied Sciences},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang
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).
@techreport{Kone15a,
title = {Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus},
author = { Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone15a.d/TR-TDgame.pdf},
year = {2015},
date = {2015-00-01},
address = {Cologne University of Applied Science},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
note = {Updated version 2015},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
2014
Thill, Markus; Konen, Wolfgang
Connect-4 Game Playing Framework (C4GPF) Miscellaneous
2014.
@misc{ThilKon14,
title = {Connect-4 Game Playing Framework (C4GPF)},
author = { Markus Thill and Wolfgang Konen},
url = {http://github.com/MarkusThill/Connect-Four},
year = {2014},
date = {2014-10-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Konen, Wolfgang; Koch, Patrick
Adaptation in Nonlinear Learning Models for Nonstationary Tasks Proceedings Article
In: Bartz-Beielstein, T.; Filipic, B. (Ed.): PPSN'2014: 13th International Conference on Parallel Problem Solving From Nature, Ljubljana, pp. 292–301, Springer, Heidelberg, 2014.
@inproceedings{Kone14a,
title = {Adaptation in Nonlinear Learning Models for Nonstationary Tasks},
author = { Wolfgang Konen and Patrick Koch},
editor = {Bartz-Beielstein, T. and Filipic, B.},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/Kone14a-Adaptation.pdf},
year = {2014},
date = {2014-09-01},
booktitle = {PPSN'2014: 13th International Conference on Parallel Problem Solving From Nature, Ljubljana},
pages = {292--301},
publisher = {Springer},
address = {Heidelberg},
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pubstate = {published},
tppubtype = {inproceedings}
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Bagheri, Samineh; Thill, Markus; Koch, Patrick; Konen, Wolfgang
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).
@techreport{Bagh14a,
title = {Online Adaptable Learning Rates for the Game Connect-4},
author = { Samineh Bagheri and Markus Thill and Patrick Koch and Wolfgang Konen},
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Thill, Markus; Bagheri, Samineh; Koch, Patrick; Konen, Wolfgang
Temporal Difference Learning with Eligibility Traces for the Game Connect-4 Proceedings Article
In: Preuss, Mike; Rudolph, Günther (Ed.): CIG'2014, International Conference on Computational Intelligence in Games, Dortmund, pp. 84 – 91, 2014.
@inproceedings{Thil14,
title = {Temporal Difference Learning with Eligibility Traces for the Game Connect-4},
author = { Markus Thill and Samineh Bagheri and Patrick Koch and Wolfgang Konen},
editor = {Preuss, Mike and Rudolph, Günther},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/ThillCIG2014.pdf},
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booktitle = {CIG'2014, International Conference on Computational Intelligence in Games, Dortmund},
pages = {84 -- 91},
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2012
Thill, Markus; Koch, Patrick; Konen, Wolfgang
Reinforcement learning with n-tuples on the game Connect-4 Proceedings Article
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (Ed.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, pp. 184–194, Springer, Heidelberg, 2012.
@inproceedings{Thil12,
title = {Reinforcement learning with n-tuples on the game Connect-4},
author = { Markus Thill and Patrick Koch and Wolfgang Konen},
editor = {Coello Coello, Carlos and Cutello, Vincenzo and others},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Thi12.d/Thil12.pdf},
year = {2012},
date = {2012-09-01},
booktitle = {PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina},
pages = {184--194},
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Thill, Markus
Reinforcement Learning mit N-Tupel-Systemen für Vier Gewinnt Masters Thesis
TH Köln -- University of Applied Sciences, 2012, (Bachelor thesis, 1st prize in Opitz award 2013, Festo award 2012, Ferchau award 2012).
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title = {Reinforcement Learning mit N-Tupel-Systemen für Vier Gewinnt},
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2011
Konen, Wolfgang
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").
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title = {Self-configuration from a Machine-Learning Perspective},
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year = {2011},
date = {2011-05-01},
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2009
Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement learning for games: failures and successes -- CMA-ES and TDL in comparision Proceedings Article
In: GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pp. 2641–2648, ACM, Montreal, Québec, Canada, 2009.
@inproceedings{Kone09a,
title = {Reinforcement learning for games: failures and successes -- CMA-ES and TDL in comparision},
author = { Wolfgang Konen and Thomas Bartz-Beielstein},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/evo-reinforce-GECCO2009.pdf},
doi = {http://doi.acm.org/10.1145/1570256.1570375},
year = {2009},
date = {2009-01-01},
booktitle = {GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference},
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Konen, Wolfgang; Bartz-Beielstein, Thomas
Evolutionsstrategien und Reinforcement Learning für strategische Brettspiele Technical Report
Cologne University of Applied Sciences 2009.
@techreport{KoneB09,
title = {Evolutionsstrategien und Reinforcement Learning für strategische Brettspiele},
author = { Wolfgang Konen and Thomas Bartz-Beielstein},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/FBericht2009-RL-Kg-Bi.pdf},
year = {2009},
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2008
Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement Learning: Insights from Interesting Failures in Parameter Selection Proceedings Article
In: Rudolph, Günter; others, (Ed.): PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund, pp. 478–487, Springer, Berlin, 2008.
@inproceedings{Kone08a,
title = {Reinforcement Learning: Insights from Interesting Failures in Parameter Selection},
author = { Wolfgang Konen and Thomas Bartz-Beielstein},
editor = {Rudolph, Günter and others},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/ppsn2008.pdf},
year = {2008},
date = {2008-09-01},
booktitle = {PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund},
pages = {478--487},
publisher = {Springer},
address = {Berlin},
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Konen, Wolfgang
Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Technical Report
Cologne University of Applied Sciences 2008.
@techreport{Kone08b,
title = {Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus},
author = { Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/TR_TDlambda2008.pdf},
year = {2008},
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Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement Learning für strategische Brettspiele Technical Report
Cologne University of Applied Sciences 2008.
@techreport{Kone08c,
title = {Reinforcement Learning für strategische Brettspiele},
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