E-LIB Suche - Ergebnisse für: Steckelmacher, Denis

1

Optimistic Reinforcement Learning-Based Skill Insertions fo..:

Liu, Gaoyuan ; de Winter, Joris ; Durodié, Yuri...
IEEE Robotics and Automation Letters. 9 (2024) 6 - p. 5974-5981 , 2024

Link: https://doi.org/10.1109/..

2

Synergistic Task and Motion Planning With Reinforcement Lea..:

Liu, Gaoyuan ; de Winter, Joris ; Steckelmacher, Denis...
IEEE Robotics and Automation Letters. 8 (2023) 5 - p. 2764-2771 , 2023

Link: https://doi.org/10.1109/..

3

Actor-critic multi-objective reinforcement learning for non..:

Reymond, Mathieu ; Hayes, Conor F. ; Steckelmacher, Denis..
Autonomous Agents and Multi-Agent Systems. 37 (2023) 2 - p. , 2023

Link: https://doi.org/10.1007/..

4

Author Correction: Reviewing machine learning of corrosion ..:

Coelho, Leonardo Bertolucci ; Zhang, Dawei ; Van Ingelgem, Yves...
npj Materials Degradation. 6 (2022) 1 - p. , 2022

Link: https://doi.org/10.1038/..

5

Reviewing machine learning of corrosion prediction in a dat..:

Coelho, Leonardo Bertolucci ; Zhang, Dawei ; Van Ingelgem, Yves...
npj Materials Degradation. 6 (2022) 1 - p. , 2022

Link: https://doi.org/10.1038/..

6

Deep learning for biosignal control: insights from basic to..:

Dillen, Arnau ; Steckelmacher, Denis ; Efthymiadis, Kyriakos...
Journal of Neural Engineering. 19 (2022) 1 - p. 011003 , 2022

Link: https://doi.org/10.1088/..

7

MoveRL: To a Safer Robotic Reinforcement Learning Environme..:

, In: Communications in Computer and Information Science; Artificial Intelligence and Machine Learning,

Liu, Gaoyuan ; De Winter, Joris ; Vanderborght, Bram.. - p. 239-253 , 2022

Link: https://doi.org/10.1007/..

8

Fast Initialization of Control Parameters using Supervised ..:

, In: 2022 IEEE Conference on Control Technology and Applications (CCTA),

Willems, Jeroen ; Eryilmaz, Kerem ; Steckelmacher, Denis... - p. 1214-1221 , 2022

Link: https://doi.org/10.1109/..

9

Synthesising Reinforcement Learning Policies Through Set-Va..:

, In: Trustworthy AI - Integrating Learning, Optimization and Reasoning; Lecture Notes in Computer Science,

Coppens, Youri ; Steckelmacher, Denis ; Jonker, Catholijn M.. - p. 163-179 , 2021

Link: https://doi.org/10.1007/..

10

Sample-Efficient Model-Free Reinforcement Learning with Off..:

, In: Machine Learning and Knowledge Discovery in Databases; Lecture Notes in Computer Science,

Steckelmacher, Denis ; Plisnier, Hélène ; Roijers, Diederik M.. - p. 19-34 , 2020

Link: https://doi.org/10.1007/..

11

Transferring Multiple Policies to Hotstart Reinforcement Le..:

Plisnier, Hélène ; Steckelmacher, Denis ; Willems, Jeroen..
http://arxiv.org/abs/2301.12820. , 2023

Link: http://arxiv.org/abs/230..

12

Dynamic Size Message Scheduling for Multi-Agent Communicati..:

Sun, Qingshuang ; Steckelmacher, Denis ; Yao, Yuan..
http://arxiv.org/abs/2306.10134. , 2023

Link: http://arxiv.org/abs/230..

13

Synthesising Reinforcement Learning Policies through Set-Va..:

Coppens, Youri ; Steckelmacher, Denis ; Jonker, Catholijn M.
http://arxiv.org/abs/2106.06009. , 2021

Link: http://arxiv.org/abs/210..

14

Dynamic Weights in Multi-Objective Deep Reinforcement Learn..:

Abels, Axel ; Roijers, Diederik D.M ; Lenaerts, Tom..
https://dipot.ulb.ac.be/dspace/bitstream/2013/291979/3/abels19a.pdf. , 2019

Link: http://hdl.handle.net/20..

15

Sample-Efficient Model-Free Reinforcement Learning with Off..:

Steckelmacher, Denis ; Plisnier, Hélène ; Roijers, Diederik M.
http://arxiv.org/abs/1903.04193. , 2019

Link: http://arxiv.org/abs/190..