Search Field
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 = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Engelhardt, Raphael C.; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Sample-based Rule Extraction for Explainable Reinforcement Learning Konferenzbeitrag
In: 8th International Conference on machine Learning, Optimization, and Data Science (LOD2022), 2022.
@inproceedings{Engel2022,
title = {Sample-based Rule Extraction for Explainable Reinforcement Learning},
author = {Raphael C. Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
year = {2022},
date = {2022-01-01},
booktitle = {8th International Conference on machine Learning, Optimization, and Data Science (LOD2022)},
keywords = {decision trees, deep learning, explainable AI, Reinforcement learning, RL3},
pubstate = {published},
tppubtype = {inproceedings}
}
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},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
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 = {AI, deep learning, Game Learning, GBG, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
2021
Engelhardt, Raphael C.; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Shedding light into the black box of reinforcement learning - extended abstract Konferenzbeitrag
In: Hammer, Barbara; Schilling, Malte; Wiskott, Laurenz (Hrsg.): Workshop on Trustworthy AI in the Wild (at: KI 2021 - German Conf. on AI), 2021.
@inproceedings{Engel2021,
title = {Shedding light into the black box of reinforcement learning - extended abstract},
author = {Raphael C. Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Barbara Hammer and Malte Schilling and Laurenz Wiskott},
url = {https://dataninja.nrw/wp-content/uploads/2021/09/1_Engelhardt_SheddingLight_Abstract.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Workshop on Trustworthy AI in the Wild (at: KI 2021 - German Conf. on AI)},
keywords = {decision trees, deep learning, explainable AI, Reinforcement learning, RL3},
pubstate = {published},
tppubtype = {inproceedings}
}
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 = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
2020
Konen, Wolfgang; Bagheri, Samineh
Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Konferenzbeitrag
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 = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
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},
year = {2020},
date = {2020-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
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 = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
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 = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
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},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
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 = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang
General Board Game Playing for Education and Research in Generic AI Game Learning Konferenzbeitrag
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 = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
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 = {BT-MT, Elo, Game Learning, GBG, Glicko, machine learning, Reinforcement learning},
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 = {BT-MT, CI, Game Learning, GBG, learning, optimization, Reinforcement learning},
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 = {BT-MT, CI, Game Learning, GBG, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
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 = {Game Learning, learning, Reinforcement learning},
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 = {Game Learning, learning, Reinforcement learning},
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},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
note = {Updated version 2015},
keywords = {Game Learning, learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
2014
Konen, Wolfgang; Koch, Patrick
Adaptation in Nonlinear Learning Models for Nonstationary Tasks Konferenzbeitrag
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},
booktitle = {PPSN'2014: 13th International Conference on Parallel Problem Solving From Nature, Ljubljana},
publisher = {Springer},
address = {Heidelberg},
keywords = {Game Learning, learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
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 = {Game Learning, learning, Reinforcement learning},
pubstate = {published},
tppubtype = {misc}
}
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},
date = {2014-01-01},
number = {TR 03/2014},
institution = {CIplus},
note = {Preprint version of the article in IEEE Transactions on Computational Intelligence and AI in Games, 2015},
keywords = {Game Learning, learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Thill, Markus; Bagheri, Samineh; Koch, Patrick; Konen, Wolfgang
Temporal Difference Learning with Eligibility Traces for the Game Connect-4 Konferenzbeitrag
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},
year = {2014},
date = {2014-01-01},
booktitle = {CIG'2014, International Conference on Computational Intelligence in Games, Dortmund},
keywords = {Game Learning, learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2009
Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement learning for games: failures and successes Konferenzbeitrag
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{Kone09ab,
title = {Reinforcement learning for games: failures and successes},
author = {Wolfgang Konen and Thomas Bartz-Beielstein},
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 = {games, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement Learning: Insights from Interesting Failures in Parameter Selection Konferenzbeitrag
In: and, Günter Rudolph (Hrsg.): PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund, S. 478–487, Springer, Berlin, 2008.
@inproceedings{Kone08ab,
title = {Reinforcement Learning: Insights from Interesting Failures in Parameter Selection},
author = {Wolfgang Konen and Thomas Bartz-Beielstein},
editor = {Günter Rudolph and et al.},
year = {2008},
date = {2008-01-01},
booktitle = {PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund},
pages = {478--487},
publisher = {Springer},
address = {Berlin},
keywords = {learning, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Search Field
25 Einträge « ‹ 1 von 3
› » 1.
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}
}
2.
Engelhardt, Raphael C.; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Sample-based Rule Extraction for Explainable Reinforcement Learning Konferenzbeitrag
In: 8th International Conference on machine Learning, Optimization, and Data Science (LOD2022), 2022.
@inproceedings{Engel2022,
title = {Sample-based Rule Extraction for Explainable Reinforcement Learning},
author = {Raphael C. Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
year = {2022},
date = {2022-01-01},
booktitle = {8th International Conference on machine Learning, Optimization, and Data Science (LOD2022)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
3.
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},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
4.
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}
}
5.
Engelhardt, Raphael C.; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Shedding light into the black box of reinforcement learning - extended abstract Konferenzbeitrag
In: Hammer, Barbara; Schilling, Malte; Wiskott, Laurenz (Hrsg.): Workshop on Trustworthy AI in the Wild (at: KI 2021 - German Conf. on AI), 2021.
@inproceedings{Engel2021,
title = {Shedding light into the black box of reinforcement learning - extended abstract},
author = {Raphael C. Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Barbara Hammer and Malte Schilling and Laurenz Wiskott},
url = {https://dataninja.nrw/wp-content/uploads/2021/09/1_Engelhardt_SheddingLight_Abstract.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Workshop on Trustworthy AI in the Wild (at: KI 2021 - German Conf. on AI)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
6.
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}
}
7.
Konen, Wolfgang; Bagheri, Samineh
Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Konferenzbeitrag
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},
tppubtype = {inproceedings}
}
8.
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},
year = {2020},
date = {2020-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
9.
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}
}
10.
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},
tppubtype = {techreport}
}
25 Einträge « ‹ 1 von 3
› »
