| Dynamic weights in multi-objective deep reinforcement learning A Abels, D Roijers, T Lenaerts, A Nowé, D Steckelmacher International conference on machine learning, 11-20, 2019 | 215 | 2019 |
| Reviewing machine learning of corrosion prediction in a data-oriented perspective LB Coelho, D Zhang, Y Van Ingelgem, D Steckelmacher, A Nowé, ... npj Materials Degradation 6 (1), 8, 2022 | 141* | 2022 |
| Multi-objective reinforcement learning for the expected utility of the return DM Roijers, D Steckelmacher, A Nowé Proceedings of the Adaptive and Learning Agents workshop at FAIM 2018, 2018 | 51 | 2018 |
| Actor-critic multi-objective reinforcement learning for non-linear utility functions M Reymond, CF Hayes, D Steckelmacher, DM Roijers, A Nowé Autonomous Agents and Multi-Agent Systems 37 (2), 23, 2023 | 36 | 2023 |
| Deep learning for biosignal control: insights from basic to real-time methods with recommendations A Dillen, D Steckelmacher, K Efthymiadis, K Langlois, A De Beir, ... Journal of Neural Engineering 19 (1), 011003, 2022 | 26 | 2022 |
| Reinforcement learning in POMDPs with memoryless options and option-observation initiation sets D Steckelmacher, D Roijers, A Harutyunyan, P Vrancx, H Plisnier, A Nowé Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 25 | 2018 |
| Sample-efficient model-free reinforcement learning with off-policy critics D Steckelmacher, H Plisnier, DM Roijers, A Nowé Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 18 | 2020 |
| Synergistic task and motion planning with reinforcement learning-based non-prehensile actions G Liu, J De Winter, D Steckelmacher, RK Hota, A Nowe, B Vanderborght IEEE Robotics and Automation Letters 8 (5), 2764-2771, 2023 | 8 | 2023 |
| Reviewing machine learning of corrosion prediction in a data-oriented perspective, npj Materials Degradation, 6 (1),(2022) LB Coelho, D Zhang, YV Ingelgem, D Steckelmacher, A Nowé, H Terryn DOI: https://doi. org/10.1038/s41529-022-00218-4, 0 | 8 | |
| Synthesising reinforcement learning policies through set-valued inductive rule learning Y Coppens, D Steckelmacher, CM Jonker, A Nowé International Workshop on the Foundations of Trustworthy AI Integrating …, 2020 | 7 | 2020 |
| Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors. H Plisnier, D Steckelmacher, DM Roijers, A Nowé BNAIC/BENELEARN, 2019 | 7 | 2019 |
| An empirical comparison of neural architectures for reinforcement learning in partially observable environments D Steckelmacher, P Vrancx arXiv preprint arXiv:1512.05509, 2015 | 5 | 2015 |
| The actor-advisor: Policy gradient with off-policy advice H Plisnier, D Steckelmacher, DM Roijers, A Nowé arXiv preprint arXiv:1902.02556, 2019 | 4 | 2019 |
| Directed policy gradient for safe reinforcement learning with human advice H Plisnier, D Steckelmacher, T Brys, DM Roijers, A Nowé arXiv preprint arXiv:1808.04096, 2018 | 3 | 2018 |
| Transferring multiple policies to hotstart reinforcement learning in an air compressor management problem H Plisnier, D Steckelmacher, J Willems, B Depraetere, A Nowé arXiv preprint arXiv:2301.12820, 2023 | 1 | 2023 |
| Explainable reinforcement learning through goal-based interpretability G Bonaert, Y Coppens, D Steckelmacher, A Nowe | 1 | 2021 |
| Self-transfer reinforcement learning for continuous control tasks H Plisnier, D Steckelmacher, A Nowé Proc. Adapt. Learn. Agents Workshop at AAMAS (ALA), 1-7, 2021 | 1 | 2021 |
| Transfer Learning Across Simulated Robots With Different Sensors H Plisnier, D Steckelmacher, D Roijers, A Nowé arXiv preprint arXiv:1907.07958, 2019 | 1 | 2019 |
| CM-GP: Critic-Moderated Genetic Programming S Deproost, D Steckelmacher | | 2025 |
| Programmatic Reinforcement Learning using Critic-Moderated Evolution S Deproost, D Steckelmacher, A Nowe BNAIC/BeNeLearn 2024: Joint International Scientific Conferences on AI and …, 2024 | | 2024 |
