Presenting our Approach to Explainable Reinforcement Learning at LOD 2022 Conference

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As previously announced, last week I had the pleasure to present our joint work with our partners from Ruhr-University Bochum on explainable reinforcement learning at the 8th Annual Conference on machine Learning, Optimization and Data science (LOD). The presentation sparked interesting questions and lead to inspiring discussions in the enchanting ambiance of the medieval monastery...

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Researchers from TH Köln present at international conference LOD 2022

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We are pleased to announce that we will present our research on explainable reinforcement learning at the 8th Annual Conference on machine Learning, Optimization and Data science (LOD). Starting with its first edition in 2015, the LOD is an established international and interdisciplinary forum for research and discussion of Deep Learning, Optimization, Big Data, and...

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More Transparency in Artificial Intelligence

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(Image: Christoph J Kellner)   For the research project (RL)^3, which is lead by Laurenz Wiskott (RUB) and Wolfgang Konen (TH Köln) and that is illustrated in the figure above, there is now a press release TH Köln available:  https://www.th-koeln.de/hochschule/mehr-transparenz-bei-kuenstlicher-intelligenz_84905.php (sorry, in German only!). (RL)^3 is part of the graduate school Dataninja (Trustworthy AI for Seamless...

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Deep Learning and Reinforcement Learning at BIOMA'2020

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We are happy to announce that the CIOP group of TH Köln participated with two papers and two talks at the 9th International Conference BIOMA'2020 (Bioinspired Optimization Methods and Applications), which took place November 2020, 19th-20th, and was this year a completely online event: "Reinforcement Learning for N-Player Games: The Importance of Final Adaptation" by...

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TH Köln takes part in AI Graduate College Data-NInJA

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The State of North Rhine-Westphalia provides a grant for the AI Graduate College Data-NInJA („Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis“), coordinated by Prof. Barbara Hammer, University Bielefeld. The grant consists of seven PhD tandems, which were selected out of 37 applications for this grant by an expert jury. TH...

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TH Köln presents at IEEE CONFERENCE ON GAMES (CoG) 2019, London, UK

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   Wolfgang Konen presented his work on General Board Game Playing at CoG'2019, the Conference on Games, which took place between 20-23 of August in London, UK. You can find the poster here and the accompanying paper here on arXiv. The General Board Game (GBG) Playing Framework is about computer agents that learn game strategies...

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General Board Game Playing as Educational Tool

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GBG (General Board Game Playing & Learning) is an Open Source software framework developed at TH Köln, University of Applied Sciences. GBG aims to ease the entry for the students into game learning and the reinforcement learning research area which is a very interesting sub-field of artifical intelligence. In 2018,

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New Technical Report on Temporal Difference Learning for Games

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A new technical report on temporal difference (TD) learning for games and "self-play" algorithms for game-agent training is available. This report by Wolfgang Konen features a gentle introduction to TD learning for game play and gives hints for the practioner on the implementation of such algorithms . It shows the references to the most recent...

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Prize for CIOP Bachelor Thesis in Reinforcement Learning

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Mr. Markus Thill has won the first price in the 2012 OPITZ CONSULTING “Innovation in Informatics” contest. Many congratulations from the CIOP team!! Mr. Thill’s thesis advanced the state of the art in reinforcement learning for complex board games, here Connect Four. Read more about his work on this page. 

Posts nach Kategorien: Reinforcement Learning