Recurrent Rational Networks. - Quentin Delfosse; Patrick Schramowski; Alejandro Molina 001; Kristian Kersting (CoRR, 2021)

Generative Adversarial Neural Cellular Automata. - Maximilian Otte; Quentin Delfosse; Johannes Czech; Kristian Kersting (CoRR, 2021)

CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability. - Martin Mundt; Steven Lang; Quentin Delfosse; Kristian Kersting (ICLR, 2022)

Adaptable Adapters. - Nafise Sadat Moosavi; Quentin Delfosse; Kristian Kersting; Iryna Gurevych (NAACL-HLT, 2022)

Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction. - Quentin Delfosse; Hikaru Shindo; Devendra Singh Dhami; Kristian Kersting (NeurIPS, 2023)

Boosting Object Representation Learning via Motion and Object Continuity. - Quentin Delfosse; Wolfgang Stammer; Thomas Rothenbacher; Dwarak Vittal; Kristian Kersting (ECML/PKDD (4), 2023)

Adaptive Rational Activations to Boost Deep Reinforcement Learning. - Quentin Delfosse; Patrick Schramowski; Martin Mundt; Alejandro Molina 001; Kristian Kersting (ICLR, 2024)

Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents. - Quentin Delfosse; Sebastian Sztwiertnia; Mark Rothermel; Wolfgang Stammer; Kristian Kersting (NeurIPS, 2024)

OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments. - Quentin Delfosse; Jannis Blüml; Bjarne Gregori; Sebastian Sztwiertnia; Kristian Kersting (RLJ, 2024)

Pix2Code: Learning to Compose Neural Visual Concepts as Programs. - Antonia Wüst; Wolfgang Stammer; Quentin Delfosse; Devendra Singh Dhami; Kristian Kersting (UAI, 2024)

Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop. - Hector Kohler; Quentin Delfosse; Paul Festor; Philippe Preux (CoRR, 2024)

Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning. - Hector Kohler; Quentin Delfosse; Riad Akrour; Kristian Kersting; Philippe Preux (CoRR, 2024)

HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning. - Quentin Delfosse; Jannis Blüml; Bjarne Gregori; Kristian Kersting (CoRR, 2024)

EXPIL: Explanatory Predicate Invention for Learning in Games. - Jingyuan Sha; Hikaru Shindo; Quentin Delfosse; Kristian Kersting; Devendra Singh Dhami (CoRR, 2024)

OCALM: Object-Centric Assessment with Language Models. - Timo Kaufmann; Jannis Blüml; Antonia Wüst; Quentin Delfosse; Kristian Kersting; Eyke Hüllermeier (CoRR, 2024)

Interpretable end-to-end Neurosymbolic Reinforcement Learning agents. - Nils Grandien; Quentin Delfosse; Kristian Kersting (CoRR, 2024)

BlendRL: A Framework for Merging Symbolic and Neural Policy Learning. - Hikaru Shindo; Quentin Delfosse; Devendra Singh Dhami; Kristian Kersting (ICLR, 2025)

Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs. - Hector Kohler; Quentin Delfosse; Waris Radji; Riad Akrour; Philippe Preux (CoRR, 2025)

Deep Reinforcement Learning via Object-Centric Attention. - Jannis Blüml; Cedric Derstroff; Bjarne Gregori; Elisabeth Dillies; Quentin Delfosse; Kristian Kersting (CoRR, 2025)

Better Decisions through the Right Causal World Model. - Elisabeth Dillies; Quentin Delfosse; Jannis Blüml; Raban Emunds; Florian Peter Busch; Kristian Kersting (CoRR, 2025)

Deep Reinforcement Learning Agents are not even close to Human Intelligence. - Quentin Delfosse; Jannis Blüml; Fabian Tatai; Théo Vincent; Bjarne Gregori; Elisabeth Dillies; Jan Peters 001; Constantin A. Rothkopf; Kristian Kersting (CoRR, 2025)