Denis Steckelmacher
Cited by
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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
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
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
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
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
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
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
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
Synthesising reinforcement learning policies through set-valued inductive rule learning
Y Coppens, D Steckelmacher, CM Jonker, A Nowé
Trustworthy AI-Integrating Learning, Optimization and Reasoning: First …, 2021
Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors.
H Plisnier, D Steckelmacher, DM Roijers, A Nowé
An empirical comparison of neural architectures for reinforcement learning in partially observable environments
D Steckelmacher, P Vrancx
arXiv preprint arXiv:1512.05509, 2015
The actor-advisor: Policy gradient with off-policy advice
H Plisnier, D Steckelmacher, DM Roijers, A Nowé
arXiv preprint arXiv:1902.02556, 2019
Reviewing machine learning of corrosion prediction in a data-oriented perspective
V Vangrunderbeek, LB Coelho, Y Van Ingelgem, H Terryn, A Nowe, ...
Reviewing machine learning of corrosion prediction in a data-oriented …, 2022
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
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
Explainable reinforcement learning through goal-based interpretability
G Bonaert, Y Coppens, D Steckelmacher, A Nowe
Transfer Learning Across Simulated Robots With Different Sensors
H Plisnier, D Steckelmacher, D Roijers, A Nowé
arXiv preprint arXiv:1907.07958, 2019
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth
Q Sun, D Steckelmacher, Y Yao, A Nowé, R Avalos
arXiv preprint arXiv:2306.10134, 2023
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
Fast Initialization of Control Parameters using Supervised Learning on Data from Similar Assets
J Willems, K Eryilmaz, D Steckelmacher, B Depraetere, R Beck, ...
2022 IEEE Conference on Control Technology and Applications (CCTA), 1214-1221, 2022
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