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ORCIDAnn Nowé
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- affiliation: Vrije Universiteit Brussel, Artificial Intelligence Lab, Belgium
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2020 – today
- 2024
[j75]Mathieu Reymond
, Conor F. Hayes, Lander Willem, Roxana Radulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin:
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. Expert Syst. Appl. 249: 123686 (2024)- [j74]Gaoyuan Liu, Joris De Winter, Yuri Durodié, Denis Steckelmacher, Ann Nowé, Bram Vanderborght:
Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning. IEEE Robotics Autom. Lett. 9(6): 5974-5981 (2024) - [j73]Diana Gomes, Kyriakos Efthymiadis, Ann Nowé, Peter Vrancx:
Depth Scaling in Graph Neural Networks: Understanding the Flat Curve Behavior. Trans. Mach. Learn. Res. 2024 (2024) - [c203]Ann Nowé:
Trustworthy Reinforcement Learning: Opportunities and Challenges. AAMAS 2024: 1 - [c202]Mathieu Reymond, Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé:
Interactively Learning the User's Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. AAMAS 2024: 1611-1620 - [c201]Alexandra Cimpean, Catholijn M. Jonker, Pieter Libin, Ann Nowé:
A Reinforcement Learning Framework for Studying Group and Individual Fairness. AAMAS 2024: 2216-2218 - [c200]Raphaël Avalos, Florent Delgrange, Ann Nowé, Guillermo A. Pérez, Diederik M. Roijers:
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. ICLR 2024 - [i51]Willem Röpke, Mathieu Reymond, Patrick Mannion, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning. CoRR abs/2402.07182 (2024) - [i50]Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling, Ann Nowé, Guillermo A. Pérez:
Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies. CoRR abs/2402.13785 (2024) - [i49]Axel Abels, Elias Fernández Domingos, Ann Nowé, Tom Lenaerts:
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News. CoRR abs/2403.08829 (2024) - [i48]Yannick Molinghen, Raphaël Avalos, Mark Van Achter, Ann Nowé, Tom Lenaerts:
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks. CoRR abs/2404.03596 (2024) - [i47]Florian Felten, Umut Ucak, Hicham Azmani, Gao Peng, Willem Röpke, Hendrik Baier, Patrick Mannion, Diederik M. Roijers, Jordan K. Terry, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Roxana Radulescu:
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning. CoRR abs/2407.16312 (2024) - [i46]Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
Online Planning in POMDPs with State-Requests. CoRR abs/2407.18812 (2024) - 2023
- [j72]Mathieu Reymond, Conor F. Hayes, Denis Steckelmacher, Diederik M. Roijers, Ann Nowé:
Actor-critic multi-objective reinforcement learning for non-linear utility functions. Auton. Agents Multi Agent Syst. 37(2): 23 (2023) - [j71]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Dealing with expert bias in collective decision-making. Artif. Intell. 320: 103921 (2023) - [j70]Nassim Versbraegen, Barbara Gravel, Charlotte Nachtegael, Alexandre Renaux, Emma Verkinderen, Ann Nowé, Tom Lenaerts, Sofia Papadimitriou:
Faster and more accurate pathogenic combination predictions with VarCoPP2.0. BMC Bioinform. 24(1): 179 (2023) - [j69]Alexandre Renaux, Chloé Terwagne, Michael Cochez, Ilaria Tiddi, Ann Nowé, Tom Lenaerts:
A knowledge graph approach to predict and interpret disease-causing gene interactions. BMC Bioinform. 24(1): 324 (2023) - [j68]Gaoyuan Liu, Joris De Winter, Denis Steckelmacher, Roshan Kumar Hota, Ann Nowé, Bram Vanderborght:
Synergistic Task and Motion Planning With Reinforcement Learning-Based Non-Prehensile Actions. IEEE Robotics Autom. Lett. 8(5): 2764-2771 (2023) - [j67]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. Trans. Mach. Learn. Res. 2023 (2023) - [c199]Willem Röpke, Carla Groenland, Roxana Radulescu, Ann Nowé, Diederik M. Roijers:
Bridging the Gap Between Single and Multi Objective Games. AAMAS 2023: 224-232 - [c198]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
A Study of Nash Equilibria in Multi-Objective Normal-Form Games. AAMAS 2023: 269-271 - [c197]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c196]Lucas Nunes Alegre, Ana L. C. Bazzan, Diederik M. Roijers, Ann Nowé, Bruno C. da Silva:
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization. AAMAS 2023: 2003-2012 - [c195]Florent Delgrange, Ann Nowé, Guillermo A. Pérez:
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees. ICLR 2023 - [c194]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. ICML 2023: 79-90 - [c193]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. IJCAI 2023: 5711-5719 - [c192]Lucas Nunes Alegre, Ana L. C. Bazzan, Ann Nowé, Bruno C. da Silva:
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models. NeurIPS 2023 - [c191]Florian Felten, Lucas N. Alegre, Ann Nowé, Ana L. C. Bazzan, El-Ghazali Talbi, Grégoire Danoy, Bruno C. da Silva:
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. NeurIPS 2023 - [e6]Kobi Gal, Ann Nowé, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu:
ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland - Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023). Frontiers in Artificial Intelligence and Applications 372, IOS Press 2023, ISBN 978-1-64368-436-9 [contents] - [i45]Willem Röpke, Carla Groenland, Roxana Radulescu, Ann Nowé, Diederik M. Roijers:
Bridging the Gap Between Single and Multi Objective Games. CoRR abs/2301.05755 (2023) - [i44]Lucas Nunes Alegre, Ana L. C. Bazzan, Diederik M. Roijers, Ann Nowé, Bruno C. da Silva:
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization. CoRR abs/2301.07784 (2023) - [i43]Hélène Plisnier, Denis Steckelmacher, Jeroen Willems, Bruno Depraetere, Ann Nowé:
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem. CoRR abs/2301.12820 (2023) - [i42]Alexandra Cimpean, Timothy Verstraeten, Lander Willem, Niel Hens, Ann Nowé, Pieter Libin:
Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration. CoRR abs/2301.12822 (2023) - [i41]Raphaël Avalos, Florent Delgrange, Ann Nowé, Guillermo A. Pérez, Diederik M. Roijers:
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. CoRR abs/2303.03284 (2023) - [i40]Florent Delgrange, Ann Nowé, Guillermo A. Pérez:
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees. CoRR abs/2303.12558 (2023) - [i39]Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke, Ann Nowé, Maarten Messagie:
Safe reinforcement learning with self-improving hard constraints for multi-energy management systems. CoRR abs/2304.08897 (2023) - [i38]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. CoRR abs/2305.01063 (2023) - [i37]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. CoRR abs/2305.05560 (2023) - [i36]Qingshuang Sun, Denis Steckelmacher, Yuan Yao, Ann Nowé, Raphaël Avalos:
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth. CoRR abs/2306.10134 (2023) - 2022
- [j66]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j65]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
On nash equilibria in normal-form games with vectorial payoffs. Auton. Agents Multi Agent Syst. 36(2): 53 (2022) - [j64]Charlotte Nachtegael, Barbara Gravel, Arnau Dillen, Guillaume Smits, Ann Nowé, Sofia Papadimitriou, Tom Lenaerts:
Scaling up oligogenic diseases research with OLIDA: the Oligogenic Diseases Database. Database J. Biol. Databases Curation 2022(2022) (2022) - [j63]Roxana Radulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent learning awareness and modelling in multi-objective normal form games. Neural Comput. Appl. 34(3): 1759-1781 (2022) - [c190]Florent Delgrange, Ann Nowé, Guillermo A. Pérez:
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes. AAAI 2022: 6497-6505 - [c189]Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Pareto Conditioned Networks. AAMAS 2022: 1110-1118 - [c188]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. AAMAS 2022: 1524-1526 - [c187]Jeroen Willems, Kerem Eryilmaz, Denis Steckelmacher, Bruno Depraetere, Rian Beck, Abdellatif Bey-Temsamani, Jan Helsen, Ann Nowé:
Fast Initialization of Control Parameters using Supervised Learning on Data from Similar Assets. CCTA 2022: 1214-1221 - [c186]Nixon K. Ronoh, Edna Milgo, Ambrose K. Kiprop, Bernard Manderick, Ann Nowé:
Natural gradient evolution strategies for adaptive sampling. GECCO Companion 2022: 73-74 - [d2]Alexandre Renaux, Ann Nowé, Tom Lenaerts:
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph. Version 1.0. Zenodo, 2022 [all versions]
- [d1]Alexandre Renaux, Ann Nowé, Tom Lenaerts:
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph. Version 1.0. Zenodo, 2022 [all versions] - [i35]Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Radulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin:
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. CoRR abs/2204.05027 (2022) - [i34]Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Pareto Conditioned Networks. CoRR abs/2204.05036 (2022) - [i33]Glenn Ceusters, Luis Ramirez Camargo, Rüdiger Franke, Ann Nowé, Maarten Messagie:
Safe reinforcement learning for multi-energy management systems with known constraint functions. CoRR abs/2207.03830 (2022) - 2021
- [j62]Ulle Endriss, Ann Nowé, Maria L. Gini, Victor R. Lesser, Michael Luck, Ana Paiva, Jaime Simão Sichman:
Autonomous agents and multiagent systems: perspectives on 20 years of AAMAS. AI Matters 7(3): 29-37 (2021) - [j61]Joris De Winter, Ilias El Makrini, Greet Van de Perre, Ann Nowé, Tom Verstraten, Bram Vanderborght:
Autonomous assembly planning of demonstrated skills with reinforcement learning in simulation. Auton. Robots 45(8): 1097-1110 (2021) - [j60]Gianluca Bontempi, Ricardo Chavarriaga, Hans ed Canck, Emanuela Girardi, Holger H. Hoos, Iarla Kilbane-Dawe, Tonio Ball, Ann Nowé, Jose Sousa, Davide Bacciu, Marco Aldinucci, Manlio ed Domenico, Alessandro Saffiotti, Marco Maratea:
The CLAIRE COVID-19 initiative: approach, experiences and recommendations. Ethics Inf. Technol. 23(S1): 127-133 (2021) - [j59]Yannick De Bock, Andres Auquilla, Ellen Bracquené, Ann Nowé, Joost R. Duflou:
The energy saving potential of retrofitting a smart heating system: A residence hall pilot study. Sustain. Comput. Informatics Syst. 31: 100585 (2021) - [c185]Gaoyuan Liu, Joris De Winter, Bram Vanderborght, Ann Nowé, Denis Steckelmacher:
MoveRL: To a Safer Robotic Reinforcement Learning Environment. BNAIC/BENELEARN 2021: 239-253 - [e5]Frank Dignum, Alessio Lomuscio, Ulle Endriss, Ann Nowé:
AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021. ACM 2021, ISBN 978-1-4503-8307-3 [contents] - [i32]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i31]Glenn Ceusters, Román Cantú Rodríguez, Alberte Bouso García, Rüdiger Franke, Geert Deconinck, Lieve Helsen, Ann Nowé, Maarten Messagie, Luis Ramirez Camargo:
Model-predictive control and reinforcement learning in multi-energy system case studies. CoRR abs/2104.09785 (2021) - [i30]Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker, Ann Nowé:
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning. CoRR abs/2106.06009 (2021) - [i29]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Dealing with Expert Bias in Collective Decision-Making. CoRR abs/2106.13539 (2021) - [i28]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
Preference Communication in Multi-Objective Normal-Form Games. CoRR abs/2111.09191 (2021) - [i27]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
On Nash Equilibria in Normal-Form Games With Vectorial Payoffs. CoRR abs/2112.06500 (2021) - [i26]Florent Delgrange, Ann Nowé, Guillermo A. Pérez:
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report). CoRR abs/2112.09655 (2021) - [i25]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2112.12458 (2021) - 2020
- [j58]Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Multi-objective multi-agent decision making: a utility-based analysis and survey. Auton. Agents Multi Agent Syst. 34(1): 10 (2020) - [j57]Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé:
AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings). J. Mach. Learn. Res. 21: 102:1-102:12 (2020) - [j56]Roxana Radulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé:
A utility-based analysis of equilibria in multi-objective normal-form games. Knowl. Eng. Rev. 35: e32 (2020) - [j55]Gabriel de Oliveira Ramos, Bruno C. da Silva, Roxana Radulescu, Ana L. C. Bazzan, Ann Nowé:
Toll-based reinforcement learning for efficient equilibria in route choice. Knowl. Eng. Rev. 35: e8 (2020) - [j54]Yannick De Bock, Andres Auquilla, Ann Nowé, Joost R. Duflou:
Nonparametric user activity modelling and prediction. User Model. User Adapt. Interact. 30(5): 803-831 (2020) - [c184]Gabriel de Oliveira Ramos, Roxana Radulescu, Ann Nowé, Anderson R. Tavares:
Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences. AAMAS 2020: 1098-1106 - [c183]Timothy Verstraeten, Eugenio Bargiacchi, Pieter J. K. Libin, Diederik M. Roijers, Ann Nowé:
Thompson Sampling for Factored Multi-Agent Bandits. AAMAS 2020: 2029-2031 - [c182]Yijie Zhang, Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games. AAMAS 2020: 2080-2082 - [c181]Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. AAMAS 2020: 2158-2160 - [c180]Timothy Verstraeten, Pieter J. K. Libin, Ann Nowé:
Fleet Control Using Coregionalized Gaussian Process Policy Iteration. ECAI 2020: 1571-1578 - [c179]Isel Grau, Dipankar Sengupta, María Matilde García Lorenzo, Ann Nowé:
An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling. FUZZ-IEEE 2020: 1-8 - [c178]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Collective Decision-Making as a Contextual Multi-armed Bandit Problem. ICCCI 2020: 113-124 - [c177]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems. ICCCI 2020: 125-138 - [c176]Yailen Martínez Jiménez, Jessica Coto Palacio, Ann Nowé:
Multi-Agent Reinforcement Learning Tool for Job Shop Scheduling Problems. OLA 2020: 3-12 - [c175]Pieter J. K. Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé:
Deep Reinforcement Learning for Large-Scale Epidemic Control. ECML/PKDD (5) 2020: 155-170 - [c174]Diederik M. Roijers, Luisa M. Zintgraf, Pieter Libin, Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. ECML/PKDD (3) 2020: 463-478 - [c173]Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker, Ann Nowé:
Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning. TAILOR 2020: 163-179 - [i24]Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé:
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping. CoRR abs/2001.07527 (2020) - [i23]Roxana Radulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé:
A utility-based analysis of equilibria in multi-objective normal form games. CoRR abs/2001.08177 (2020) - [i22]Isel Grau, Dipankar Sengupta, María Matilde García Lorenzo, Ann Nowé:
An interpretable semi-supervised classifier using two different strategies for amended self-labeling. CoRR abs/2001.09502 (2020) - [i21]Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé:
Deep reinforcement learning for large-scale epidemic control. CoRR abs/2003.13676 (2020) - [i20]Roxana Radulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games. CoRR abs/2011.07290 (2020)
2010 – 2019
- 2019
- [j53]Oliver Roesler, Ann Nowé:
Action learning and grounding in simulated human-robot interactions. Knowl. Eng. Rev. 34: e13 (2019) - [j52]Alexandre Renaux, Sofia Papadimitriou, Nassim Versbraegen, Charlotte Nachtegael, Simon Boutry, Ann Nowé, Guillaume Smits, Tom Lenaerts:
ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations. Nucleic Acids Res. 47(Webserver-Issue): W93-W98 (2019) - [j51]Joris De Winter, Albert De Beir, Ilias El Makrini, Greet Van de Perre, Ann Nowé, Bram Vanderborght:
Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot. Robotics 8(4): 104 (2019) - [c172]Youri Coppens, Eugenio Bargiacchi, Ann Nowé:
A Virtual Maze Game to Explain Reinforcement Learning. BNAIC/BENELEARN 2019 - [c171]Jannick Hemelhof, Mihail Mihaylov, Ann Nowé:
Improving Zero-Intelligence Plus for Call Markets. BNAIC/BENELEARN 2019 - [c170]Pieter Libin, Nassim Versbraegen, Ana B. Abecasis, Perpetua Gomes, Tom Lenaerts, Ann Nowé:
Towards a Phylogenetic Measure to Quantify HIV Incidence. BNAIC/BENELEARN 2019 - [c169]Pieter Libin, Nassim Versbraegen, Ana B. Abecasis, Perpetua Gomes, Tom Lenaerts, Ann Nowé:
Towards a Phylogenetic Measure to Quantify HIV Incidence. BNAIC/BENELEARN (Selected Papers) 2019: 34-50 - [c168]Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Wenjia Wang, Kristof Theys, Ann Nowé:
Thompson Sampling for m-top Exploration. BNAIC/BENELEARN 2019 - [c167]Regis Loeb, Timothy Verstraeten, Ann Nowé, Ann Dooms:
Privacy Preserving Reinforcement Learning over Distributed Datasets. BNAIC/BENELEARN 2019 - [c166]Jessica Coto Palacio, Yailen Martínez Jiménez, Ann Nowé:
Multi-Agent Reinforcement Learning Tool for Job Shop Scheduling Problems. BNAIC/BENELEARN 2019 - [c165]Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers, Ann Nowé:
Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors. BNAIC/BENELEARN 2019 - [c164]Oliver Roesler, Ann Nowé:
Action Learning and Grounding in Simulated Human-Robot Interactions. BNAIC/BENELEARN 2019 - [c163]Willem Röpke, Roxana Radulescu, Kyriakos Efthymiadis, Ann Nowé:
Training a Speech-to-Text Model for Dutch on the Corpus Gesproken Nederlands. BNAIC/BENELEARN 2019 - [c162]Willem Röpke, Roxana Radulescu, Kyriakos Efthymiadis, Ann Nowé:
DuStt - A Speech-to-Text Engine for Dutch. BNAIC/BENELEARN 2019 - [c161]Denis Steckelmacher, Hélène Plisnier, Ann Nowé:
A Motorized Wheelchair that Learns to Make its Way through a Crowd. BNAIC/BENELEARN 2019 - [c160]Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers, Ann Nowé:
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. BNAIC/BENELEARN 2019 - [c159]Timothy Verstraeten, Ann Nowé, Jan Helsen:
Failure Avoidance for Wind Turbines through Fleetwide Control. BNAIC/BENELEARN 2019 - [c158]Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten, Ann Nowé:
Deep hybrid approach for 3D plane segmentation. ESANN 2019 - [c157]Beatriz M. Méndez-Hernández, Erick D. Rodríguez Bazan, Yailen Martínez Jiménez, Pieter Libin, Ann Nowé:
A Multi-objective Reinforcement Learning Algorithm for JSSP. ICANN (1) 2019: 567-584 - [c156]Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher:
Dynamic Weights in Multi-Objective Deep Reinforcement Learning. ICML 2019: 11-20 - [c155]Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup:
Per-Decision Option Discounting. ICML 2019: 2644-2652 - [c154]