+++ Update 10/2020: FutureWorkGBG: Project themes in GBG for students (PDF) +++
(see also: GBG: General Board Games in CIOP )
Games are interesting in computer science concerning the question whether a computer can learn the game strategies just from self-play, without explicitly programming the tactics or performing exhaustive search. This is a branch of artificial intelligence (AI). Many game learning approaches are based on reinforcement learning, namely TD-learning.
In our research group we have studied extensively the game Connect-4 ("Four-in-a-Row"). We were able to develop an agent which learns Connect-4 nearly perfectly just from self-play. Our Java-based Connect-4 Game Playing Framework (C4GPF) is open-source for interested researchers. Read more...
In January'2018 we released GBG, the General Board Game playing and learning framework to the research community as another open-source project.
https://github.com/WolfgangKonen/GBG
Read more about GBG ...
The long-term goal of our research group is it to transfer these learning strategies to many other games (dots-and-boxes, go, Poker, checkers, Abalone, Sim, Othello, ...). The project is related to the research field known as General Game Playing (GGP). The aim of GGP is it to develop agents which are able to learn a great variety of games.
People
Wolfgang Konen, Markus Thill, Samineh Bagheri, Johannes Kutsch, Kevin Galitzki, Felix Barsnick, Julian Cöln, Yannick Dittmar, Johannes Scheiermann
Publications
2023
Konen, Wolfgang
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning Artikel
In: arXiv preprint arXiv:2301.12167, 2023.
@article{Konen2023,
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},
urldate = {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 Abschlussarbeit
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 Forschungsbericht
TH Köln 2022.
@techreport{Konen2022,
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}
}
Weitz, Ann
Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath Abschlussarbeit
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},
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pubstate = {published},
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Weitz, Ann
Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework Forschungsbericht
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},
school = {TH Köln – University of Applied Sciences},
note = {Praxisprojekt},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
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Cöln, Julian
KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!" Abschlussarbeit
TH Köln – University of Applied Sciences, 2022, (Bachelor thesis).
@mastersthesis{Coeln2022,
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},
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Scheiermann, Johannes; Konen, Wolfgang
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel
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},
journal = {arXiv preprint arXiv:2204.13307},
note = {Preprint of the IEEE ToG 2022 paper},
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Scheiermann, Johannes; Konen, Wolfgang
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel
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},
urldate = {2022-01-01},
journal = {IEEE Transactions on Games},
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) Abschlussarbeit
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},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
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pubstate = {published},
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Zeh, Tim
Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Abschlussarbeit
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 = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_Zeh_final_Poker-GBG-2021.pdf},
year = {2021},
date = {2021-07-01},
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Konen, Wolfgang; Bagheri, Samineh
Final adaptation reinforcement learning for N-player games Artikel
In: arXiv preprint arXiv:2111.14375, 2021.
@article{Konen2021,
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
Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Forschungsbericht
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},
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}
Scheiermann, Johannes
AlphaZero-inspirierte KI-Agenten im General Board Game Playing Abschlussarbeit
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 = {2020-01-01},
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note = {Bachelor thesis},
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Konen, Wolfgang; Bagheri, Samineh
Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Proceedings Article
In: Vasile, Bogdan Filipic Massimiliano (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA), Bruxelles, 2020.
@inproceedings{Konen20bc,
title = {Reinforcement Learning for N-Player Games: The Importance of Final Adaptation},
author = {Wolfgang Konen and Samineh Bagheri},
editor = {Bogdan Filipic Massimiliano Vasile},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen20b.d/bioma20-TDNTuple.pdf},
year = {2020},
date = {2020-01-01},
booktitle = {9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA)},
address = {Bruxelles},
keywords = {},
pubstate = {published},
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Konen, Wolfgang; Bagheri, Samineh
Final Adaptation Reinforcement Learning for N-Player Games Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.
@techreport{Konen20b_TRb,
title = {Final Adaptation Reinforcement Learning for N-Player Games},
author = {Wolfgang Konen and Samineh Bagheri},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen20b_TR.d/Konen20b_TR.pdf},
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Konen, Wolfgang
The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.
@techreport{Konen20d,
title = {The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning},
author = {Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen20d.d/TR-GBG.pdf},
year = {2020},
date = {2020-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
2019
Cöln, Julian; Dittmar, Yannick
Untersuchung von KI Agenten im Spiel Othello Forschungsbericht
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 = {2019-12-01},
institution = {TH Köln, Institut für Informatik},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang
General Board Game Playing for Education and Research in Generic AI Game Learning Proceedings Article
In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): 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}
}
Barsnick, Felix
Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen Abschlussarbeit
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}
}
2017
Galitzki, Kevin
Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework Abschlussarbeit
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 = {2017-01-01},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Kutsch, Johannes
KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele Abschlussarbeit
TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis).
@mastersthesis{Kutsch2017,
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},
keywords = {},
pubstate = {published},
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}
2015
Konen, Wolfgang
Reinforcement Learning for Board Games: The Temporal Difference Algorithm Forschungsbericht
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 = {Konen, Wolfgang},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone15c.d/TR-TDgame_EN.pdf},
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}
}
Bagheri, Samineh; Thill, Markus; Koch, Patrick; Konen, Wolfgang
Online Adaptable Learning Rates for the Game Connect-4 Artikel
In: IEEE Transactions on Computational Intelligence and AI in Games, Bd. (accepted 11/2014), S. 1, 2015.
@article{Bagh15,
title = {Online Adaptable Learning Rates for the Game Connect-4},
author = {Samineh Bagheri and Markus Thill and Patrick Koch and Wolfgang Konen},
url = {http://dx.doi.org/10.1109/TCIAIG.2014.2367105},
year = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
volume = {(accepted 11/2014)},
pages = {1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Konen, Wolfgang
Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 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 = {2015-01-01},
address = {Cologne University of Applied Sciences},
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note = {Updated version 2015},
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2014
Bagheri, Samineh; Thill, Markus; Koch, Patrick; Konen, Wolfgang
Online Adaptable Learning Rates for the Game Connect-4 Forschungsbericht
CIplus Nr. TR 03/2014, 2014, (Preprint version of the article in IEEE Transactions on Computational Intelligence and AI in Games, 2015).
@techreport{Bagh14a,
title = {Online Adaptable Learning Rates for the Game Connect-4},
author = {Samineh Bagheri and Markus Thill and Patrick Koch and Wolfgang Konen},
url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos-704},
year = {2014},
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Konen, Wolfgang; Koch, Patrick
Adaptation in Nonlinear Learning Models for Nonstationary Tasks Proceedings Article
In: Filipic, Bogdan (Hrsg.): PPSN'2014: 13th International Conference on Parallel Problem Solving From Nature, Ljubljana, Springer, Heidelberg, 2014.
@inproceedings{Kone14a,
title = {Adaptation in Nonlinear Learning Models for Nonstationary Tasks},
author = {Wolfgang Konen and Patrick Koch},
editor = {Bogdan Filipic},
url = {http://maanvs03.gm.fh-koeln.de/webpub/CIOPReports.d/Kone14a.d/Kone14a.pdf},
year = {2014},
date = {2014-01-01},
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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 (Hrsg.): CIG'2014, International Conference on Computational Intelligence in Games, Dortmund, 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 = {Mike Preuss and Günther Rudolph},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/ThillCIG2014.pdf},
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Thill, Markus; Konen, Wolfgang
Connect-4 Game Playing Framework (C4GPF) Sonstige
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 = {2014-01-01},
keywords = {},
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}
2012
Thill, Markus
Reinforcement Learning mit N-Tupel-Systemen für Vier Gewinnt Abschlussarbeit
TH Köln – University of Applied Sciences, 2012, (Bachelor thesis, 1st prize in Opitz award 2013, Festo award 2012, Ferchau award 2012).
@mastersthesis{Thill2012,
title = {Reinforcement Learning mit N-Tupel-Systemen für Vier Gewinnt},
author = {Markus Thill},
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Thill, Markus; Koch, Patrick; Konen, Wolfgang
Reinforcement learning with n-tuples on the game Connect-4 Proceedings Article
In: Coello, Carlos A. Coello; Cutello, Vincenzo (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 184–194, Springer, Heidelberg, 2012.
@inproceedings{Thil12b,
title = {Reinforcement learning with n-tuples on the game Connect-4},
author = {Markus Thill and Patrick Koch and Wolfgang Konen},
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pages = {184--194},
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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, S. 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},
year = {2009},
date = {2009-01-01},
booktitle = {GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference},
pages = {2641--2648},
publisher = {ACM},
address = {Montreal, Québec, Canada},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
Konen, Wolfgang
Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Forschungsbericht
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},
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Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement Learning für strategische Brettspiele Forschungsbericht
Cologne University of Applied Sciences 2008.
@techreport{Kone08c,
title = {Reinforcement Learning für strategische Brettspiele},
author = {Wolfgang Konen and Thomas Bartz-Beielstein},
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}