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...

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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...

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Two papers accepted for our second participation at LOD 2023 conference

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For the second time we (Raphael Engelhardt and Wolfgang Konen) have been given the opportunity to present our work at the Conference on machine Learning, Optimization and Data science (LOD) conference. To this year's 9th edition, held in Grasmere, England, UK on September 22nd - 26th we have the honor to contribute even two papers...

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AI in Nuclear Power Plant Simulation: Steinmüller Engineering Award for Niklas Fabig

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The operation of nuclear power plants (NPPs) is one of the most safety-critical tasks in industry. Prior to using AI methods in this area, it should be thoroughly investigated and evaluated via simulations, whether AI can learn (e.g.´, by reinforcment learning, RL) to power up and shut down a nuclear reactor and how well such...

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