Tim Brys

Vrije Universiteit Brussel — Belgium

Computer Science · Physical Sciences

14h-index707citations51works0.02yr avg
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Research Topics

Reinforcement Learning in Robotics(28), Advanced Multi-Objective Optimization Algorithms(11), Evolutionary Algorithms and Applications(10), Metaheuristic Optimization Algorithms Research(9), Robot Manipulation and Learning(6)

Publications51 total

Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination
Proceedings of the AAAI Conference on Artificial Intelligence·2021Open Access
Introspective <i>Q</i> -learning and learning from demonstration
The Knowledge Engineering Review·2019· 2 cited
Directed Policy Gradient for Safe Reinforcement Learning with Human\n Advice
arXiv (Cornell University)·2018Open Access
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
arXiv (Cornell University)·2018· 1 citedOpen Access
Introspective Reinforcement Learning and Learning from Demonstration
2018· 4 cited
Adapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees
Proceedings of the AAAI Conference on Artificial Intelligence·2018· 27 citedOpen Access
Multi-objectivization and ensembles of shapings in reinforcement learning
Neurocomputing·2017· 37 citedOpen Access
Preface to the special issue: adaptive and learning agents
The Knowledge Engineering Review·2017Open Access
Learning from Demonstration for Shaping through Inverse Reinforcement Learning
Adaptive Agents and Multi-Agents Systems·2016· 58 cited
Human Guided Ensemble Learning in StarCraft
VUBIR (Vrije Universiteit Brussel)·2016
Dimensionality Reduced Reinforcement Learning for Assistive Robots.
National Conference on Artificial Intelligence·2016· 10 cited
A Gentle Introduction to Reinforcement Learning
Lecture notes in computer science·2016· 25 cited
Departure MANagement with a Reinforcement Learning Approach: Respecting CFMU Slots
2015· 6 cited
Reinforcement learning from demonstration through shaping
2015· 156 cited
Encoding and combining knowledge to speed up reinforcement learning
VUBIR (Vrije Universiteit Brussel)·2015· 1 cited
Shaping Mario with Human Advice
2015
Multi-Scale Reward Shaping via an Off-Policy Ensemble
Adaptive Agents and Multi-Agents Systems·2015· 3 cited
Shaping Mario with Human Advice
Adaptive Agents and Multi-Agents Systems·2015· 12 cited
Policy Transfer using Reward Shaping
Adaptive Agents and Multi-Agents Systems·2015· 51 cited
Using PCA to Efficiently Represent State Spaces
arXiv (Cornell University)·2015· 14 citedOpen Access
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Frequent Co-authors

Ann Nowé(38), Matthew E. Taylor(15), Anna Harutyunyan(14), Peter Vrancx(13), Kristof Van Moffaert(5), Mădălina M. Drugan(5), Martine De Cock(5), Daniel Kudenko⋆(4), Peter A. N. Bosman(4), Yann-Michaël De Hauwere(4), Halit Bener Suay(3), Sonia Chernova(3), Tong Pham(2), Steven Adriænsen(2), William J. Curran(2), William D. Smart(2), Mao Li(2), Hélène Plisnier(2), Denis Steckelmacher(2), Diederik M. Roijers(2)