Iterative Decision Tree Learning in the Real World

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As mentioned in a previous blog post, we developed an iterative algorithm for training decision trees (DTs) from trained deep reinforcement learning (DRL) agents. The algorithm combines the simple structure of DTs and the predictive power of well-performing DRL agents. In our publication, we tested the idea on seven different control problems and successfully trained...

Dataninja-Tandem wins Best Paper Award at LOD 2023 conference

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Our participation in this year's edition of the LOD conference, as previously announced in one of our blog post, proved to be an exceptionally enjoyable experience. The systematic evaluation of auxiliary tasks in reinforcement learning published in “Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison” by first author Moritz Lange (Dataninja-colleague...

Article Published in Special Issue of "Algorithms"

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We are delighted to announce that our article “Iterative Oblique Decision Trees Deliver Explainable RL Models” was accepted and is now part of the special issue “Advancements in Reinforcement Learning Algorithms” in the MDPI journal Algorithms (impact factor 2.2, CiteScore 3.7) . Explainability in AI and RL (known as XAI and XRL) becomes increasingly important....

Presenting our Work at KI 2021

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In September 2021, shortly after the Dataninja retreat, we participated at the KI 2021 – 44th German Conference on Artificial Intelligence. Alongside fellow members of the Dataninja research training group we, Raphael Engelhardt and Wolfgang Konen from TH Köln together with Laurenz Wiskott and Moritz Lange from RUB Bochum, presented our work on rule extraction...