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Classified by Research Category

Reinforcement LearningEvolutionary ComputationMulti-ObjectiveReward ShapingMulti-Agent


Reinforcement Learning

  1. Halit Bener Suay, Tim Brys, Matthew E. Taylor, and Sonia Chernova. Learning from Demonstration for Shaping through Inverse Reinforcement Learning. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2016.
    Details     BibTeX     Download: [pdf] (2.4MB )  
  2. Tim Brys, Anna Harutyunyan, Matthew E. Taylor, and Ann Nowé. Policy Transfer using Reward Shaping. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015.
    Details     BibTeX     Download: [pdf] (452.1kB )  
  3. Tim Brys, Anna Harutyunyan, Matthew E. Taylor, and Ann Nowé. Ensembles of Shapings. In Proceedings of the Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.
    Details     BibTeX     Download: [pdf] (622.7kB )  
  4. Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, and Ann Nowé. Reinforcement Learning from Demonstration through Shaping. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2015.
    Details     BibTeX     Download: [pdf] (372.3kB )  
  5. Tim Brys. Encoding and Combining Knowledge to Speed up Reinforcement Learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2015.
    Details     BibTeX     Download: (unavailable)
  6. William Curran, Tim Brys, Matthew E. Taylor, and William Smart. Using PCA to Efficiently Represent State Spaces. In Proceedings of the European Workshop on Reinforcement Learning (EWRL), 2015.
    Details     BibTeX     Download: (unavailable)
  7. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowé. Shaping Mario with Human Advice (Demonstration). In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015.
    Details     BibTeX     Download: [pdf] (400.4kB )  
  8. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowé. Multi-Scale Reward Shaping via an Off-Policy Ensemble. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015.
    Details     BibTeX     Download: [pdf] (224.1kB )  
  9. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowé. Off-Policy Reward Shaping with Ensembles. In Proceedings of the Adaptive Learning Agents Workshop at AAMAS, 2015.
    Details     BibTeX     Download: [pdf] (559.6kB )  
  10. Kristof Van Moffaert, Tim Brys, and Ann Nowé. Risk-Sensitivity Through Multi-Objective Reinforcement Learning. In Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2015.
    Details     BibTeX     Download: [pdf] (1016.4kB )  
  11. Silvio Rodrigues, Tim Brys, Rodrigo Teixeira Pinto, Ann Nowé, and Pavol Bauer. Online Distributed Voltage Control of an Offshore MTdc Network using Reinforcement Learning. In Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2015.
    Details     BibTeX     Download: [pdf] (391.4kB )  
  12. Ivomar Brito Soares, Yann-Michaël De Hauwere, Kris Januarius, Tim Brys, Thierry Salvant, and Ann Nowé. Departure MANagement with a Reinforcement Learning Approach: Re- specting CFMU Slots. In Proceedings of the IEEE International Conference on Intelligent Trans- portation Systems (ITSC), 2015.
    Details     BibTeX     Download: (unavailable)
  13. Halit Bener Suay, Tim Brys, Matthew E. Taylor, and Sonia Chernova. Reward Shaping by Demonstration. In Proceedings of the Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.
    Details     BibTeX     Download: [pdf] (322.3kB )  
  14. Tim Brys, Kristof Van Moffaert, Ann Nowé, and Matthew E. Taylor. Adaptive Objective Selection for Correlated Objectives in Multi-Objective Reinforcement Learning. In Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). Extended abstract, 2014.
    Details     BibTeX     Download: [pdf] (182.1kB )  
  15. Tim Brys, Tong T. Pham, and Matthew E. Taylor. Distributed learning and multi-objectivity in traffic light control. Connection Science, 26(1):65–83, Taylor & Francis, 2014.
    Details     BibTeX     Download: [pdf] (891.2kB )  
  16. Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, and Ann Nowé. Multi-Objectivization of Reinforcement Learning Problems by Reward Shaping. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014.
    Details     BibTeX     Download: [pdf] (524.2kB )  
  17. Tim Brys, Daniel Kudenko, Ann Nowé, and Matthew E. Taylor. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI)., 2014.
    Details     BibTeX     Download: [pdf] (529.7kB )  
  18. Tim Brys and Ann Nowé. Reinforcement Learning on Multiple Correlated Signals. In Proceedings of the Twenty-Eight AAAI Conference on Artificial Intelligence (AAAI), 2014.
    Details     BibTeX     Download: [pdf] (78.0kB )  
  19. Tim Brys, Matthew E. Taylor, and Ann Nowé. Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems. In Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI), 2014.
    Details     BibTeX     Download: (unavailable)
  20. Tim Brys, Daniel Kudenko, Ann Nowé, and Matthew E. Taylor. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the 26th Benelux Conference on Artificial Intelligence, 2014.
    Details     BibTeX     Download: (unavailable)
  21. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowe. Off-Policy Shaping Ensembles in Reinforcement Learning. In Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI), 2014.
    Details     BibTeX     Download: (unavailable)
  22. Kristof Van Moffaert, Tim Brys, Arjun Chandra, Lukas Esterle, Peter Lewis, and Ann Nowé. A Novel Adaptive Weight Selection Algorithm for Multi-Objective Multi-Agent Reinforcement Learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014.
    Details     BibTeX     Download: [pdf] (3.7MB )  
  23. Kristof Van Moffaert, Tim Brys, and Ann Nowé. Efficient Weight Space Search in Multi-Objective Reinforcement Learning. Technical Report AI-TR-14-69, AI Lab, Vrije Universiteit Brussel, 2014.
    Details     BibTeX     Download: [pdf] (1.3MB )  
  24. Tim Brys, Kristof Van Moffaert, Kevin Van Vaerenbergh, and Ann Nowé. On the Behaviour of Scalarization Methods for the Engagement of a Wet Clutch. In International Conference on Machine Learning and Applications (ICMLA), IEEE, 2013.
    Details     BibTeX     Download: [pdf] (1.8MB )  
  25. Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, and Ann Nowé. Multi-Objectivization in Reinforcement Learning. Technical Report AI-TR-13-354, AI Lab, Vrije Universiteit Brussel, 2013.
    Details     BibTeX     Download: (unavailable)
  26. Tong T. Pham, Tim Brys, and Matthew E. Taylor. Learning Coordinated Traffic Light Control. In Proceedings of the Adaptive and Learning Agents workshop at AAMAS, May 2013.
    ALA-13
    Details     BibTeX     Download: [pdf] (471.3kB )  
  27. Tim Brys, Yann-Michaël De Hauwere, Ann Nowé, and Peter Vrancx. Local Coordination in Online Distributed Constraint Optimization Problems. In Massimo Cossentino, Michael Kaisers, Karl Tuyls, and Gerhard Weiss, editors, Multi-Agent Systems, Lecture Notes in Computer Science, pp. 31–47, Springer Berlin / Heidelberg, 2012.
    Details     BibTeX     Download: [pdf] (840.6kB )  
  28. Tim Brys, Yann-Michaël De Hauwere, Ann Nowé, and Peter Vrancx. Local Coordination in Online Distributed Constraint Optimization Problems. In The 9th European Workshop on Multi-Agent Systems, Maastricht, The Netherlands, 2011.
    Details     BibTeX     Download: (unavailable)

Evolutionary Computation

  1. Steven Adriaensen, Tim Brys, and Ann Nowé. Fair-Share ILS: A Simple State-of-the-art Iterated Local Search Hyperheuristic. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2014.
    Details     BibTeX     Download: [pdf] (284.2kB )  
  2. Steven Adriaensen, Tim Brys, and Ann Nowé. Designing Reusable Metaheuristic Methods: A Semi-automated Approach . In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2014.
    Details     BibTeX     Download: [pdf] (339.8kB )  
  3. Steven Adriaensen, Tim Brys, and Ann Nowé. Fair-Share ILS: A Simple State-of-the-art Iterated Local Search Hyperheuristic. In Proceedings of the 26th Benelux Conference on Artificial Intelligence, 2014.
    Details     BibTeX     Download: (unavailable)
  4. Tim Brys, Madalina M. Drugan, Peter A. N. Bosman, Martine De Cock, and Ann Nowé. Local Search and Restart Strategies for Satisfiability Solving in Fuzzy Logics. In Proceedings of the 6th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) at the IEEE Symposium Series on Computational Intelligence (SSCI), pp. 52–59, April 2013.
    Nominated for Best Paper Award of the whole IEEE SSCI 2013 conference.
    Details     BibTeX     Download: [pdf] (4.2MB )  
  5. Tim Brys, Madalina M. Drugan, Peter A. N. Bosman, Martine De Cock, and Ann Nowé. Solving Satisfiability in Fuzzy Logics by Mixing CMA-ES. In Genetic and Evolutionary Computation Conference (GECCO), pp. 1125–1132, 2013.
    Best Paper Award in the combined IGEC, ESEP and BIO tracks.
    Details     BibTeX     Download: [pdf] (1.3MB )  
  6. Tim Brys, Madalina M. Drugan, and Ann Nowé. Meta-Evolutionary Algorithms and Recombination Operators for Satisfiability Solving in Fuzzy Logics. In Congress on Evolutionary Computation (CEC), pp. 1060–1067, IEEE, 2013.
    Details     BibTeX     Download: [pdf] (654.6kB )  
  7. Tim Brys, Madalina M. Drugan, Peter A. N. Bosman, Martine De Cock, and Ann Nowé. Solving Satisfiability in Fuzzy Logics by Mixing CMA-ES. In Proceedings of the 25th Benelux Conference on Artificial Intelligence, 2013.
    Previously published results (B-Paper).
    Details     BibTeX     Download: (unavailable)
  8. Tim Brys and Ann Nowé. Improving convergence of CMA-ES through structure-driven discrete recombination. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), pp. 2415–2416, 2012.
    Details     BibTeX     Download: [pdf] (132.0kB )  
  9. Tim Brys, Yann-Michaël De Hauwere, Martine De Cock, and Ann Nowé. Solving Satisfiability in Fuzzy Logics with Evolution Strategies. In Proceedings of the 31st Annual North American Fuzzy Information Processing Society Meeting, pp. 1–6, 2012.
    Best Student Paper Award.
    Details     BibTeX     Download: [pdf] (582.6kB )  
  10. Tim Brys, Yann-Michaël De Hauwere, Martine De Cock, and Ann Nowé. Solving Satisfiability in Fuzzy Logics with Evolution Strategies. In Proceedings of the 24th Benelux Conference on Artificial Intelligence, 2012.
    Previously published results (B-Paper).
    Details     BibTeX     Download: (unavailable)

Multi-Objective

  1. Kristof Van Moffaert, Tim Brys, and Ann Nowé. Risk-Sensitivity Through Multi-Objective Reinforcement Learning. In Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2015.
    Details     BibTeX     Download: [pdf] (1016.4kB )  
  2. Tim Brys, Kristof Van Moffaert, Ann Nowé, and Matthew E. Taylor. Adaptive Objective Selection for Correlated Objectives in Multi-Objective Reinforcement Learning. In Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). Extended abstract, 2014.
    Details     BibTeX     Download: [pdf] (182.1kB )  
  3. Tim Brys, Tong T. Pham, and Matthew E. Taylor. Distributed learning and multi-objectivity in traffic light control. Connection Science, 26(1):65–83, Taylor & Francis, 2014.
    Details     BibTeX     Download: [pdf] (891.2kB )  
  4. Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, and Ann Nowé. Multi-Objectivization of Reinforcement Learning Problems by Reward Shaping. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014.
    Details     BibTeX     Download: [pdf] (524.2kB )  
  5. Tim Brys, Daniel Kudenko, Ann Nowé, and Matthew E. Taylor. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI)., 2014.
    Details     BibTeX     Download: [pdf] (529.7kB )  
  6. Tim Brys and Ann Nowé. Reinforcement Learning on Multiple Correlated Signals. In Proceedings of the Twenty-Eight AAAI Conference on Artificial Intelligence (AAAI), 2014.
    Details     BibTeX     Download: [pdf] (78.0kB )  
  7. Tim Brys, Daniel Kudenko, Ann Nowé, and Matthew E. Taylor. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the 26th Benelux Conference on Artificial Intelligence, 2014.
    Details     BibTeX     Download: (unavailable)
  8. Kristof Van Moffaert, Tim Brys, Arjun Chandra, Lukas Esterle, Peter Lewis, and Ann Nowé. A Novel Adaptive Weight Selection Algorithm for Multi-Objective Multi-Agent Reinforcement Learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014.
    Details     BibTeX     Download: [pdf] (3.7MB )  
  9. Kristof Van Moffaert, Tim Brys, and Ann Nowé. Efficient Weight Space Search in Multi-Objective Reinforcement Learning. Technical Report AI-TR-14-69, AI Lab, Vrije Universiteit Brussel, 2014.
    Details     BibTeX     Download: [pdf] (1.3MB )  
  10. Tim Brys, Kristof Van Moffaert, Kevin Van Vaerenbergh, and Ann Nowé. On the Behaviour of Scalarization Methods for the Engagement of a Wet Clutch. In International Conference on Machine Learning and Applications (ICMLA), IEEE, 2013.
    Details     BibTeX     Download: [pdf] (1.8MB )  
  11. Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, and Ann Nowé. Multi-Objectivization in Reinforcement Learning. Technical Report AI-TR-13-354, AI Lab, Vrije Universiteit Brussel, 2013.
    Details     BibTeX     Download: (unavailable)

Reward Shaping

  1. Halit Bener Suay, Tim Brys, Matthew E. Taylor, and Sonia Chernova. Learning from Demonstration for Shaping through Inverse Reinforcement Learning. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2016.
    Details     BibTeX     Download: [pdf] (2.4MB )  
  2. Tim Brys, Anna Harutyunyan, Matthew E. Taylor, and Ann Nowé. Policy Transfer using Reward Shaping. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015.
    Details     BibTeX     Download: [pdf] (452.1kB )  
  3. Tim Brys, Anna Harutyunyan, Matthew E. Taylor, and Ann Nowé. Ensembles of Shapings. In Proceedings of the Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.
    Details     BibTeX     Download: [pdf] (622.7kB )  
  4. Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, and Ann Nowé. Reinforcement Learning from Demonstration through Shaping. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2015.
    Details     BibTeX     Download: [pdf] (372.3kB )  
  5. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowé. Shaping Mario with Human Advice (Demonstration). In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015.
    Details     BibTeX     Download: [pdf] (400.4kB )  
  6. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowé. Multi-Scale Reward Shaping via an Off-Policy Ensemble. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015.
    Details     BibTeX     Download: [pdf] (224.1kB )  
  7. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowé. Off-Policy Reward Shaping with Ensembles. In Proceedings of the Adaptive Learning Agents Workshop at AAMAS, 2015.
    Details     BibTeX     Download: [pdf] (559.6kB )  
  8. Halit Bener Suay, Tim Brys, Matthew E. Taylor, and Sonia Chernova. Reward Shaping by Demonstration. In Proceedings of the Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.
    Details     BibTeX     Download: [pdf] (322.3kB )  
  9. Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, and Ann Nowé. Multi-Objectivization of Reinforcement Learning Problems by Reward Shaping. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014.
    Details     BibTeX     Download: [pdf] (524.2kB )  
  10. Tim Brys, Daniel Kudenko, Ann Nowé, and Matthew E. Taylor. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI)., 2014.
    Details     BibTeX     Download: [pdf] (529.7kB )  
  11. Tim Brys and Ann Nowé. Reinforcement Learning on Multiple Correlated Signals. In Proceedings of the Twenty-Eight AAAI Conference on Artificial Intelligence (AAAI), 2014.
    Details     BibTeX     Download: [pdf] (78.0kB )  
  12. Tim Brys, Matthew E. Taylor, and Ann Nowé. Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems. In Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI), 2014.
    Details     BibTeX     Download: (unavailable)
  13. Tim Brys, Daniel Kudenko, Ann Nowé, and Matthew E. Taylor. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the 26th Benelux Conference on Artificial Intelligence, 2014.
    Details     BibTeX     Download: (unavailable)
  14. Anna Harutyunyan, Tim Brys, Peter Vrancx, and Ann Nowe. Off-Policy Shaping Ensembles in Reinforcement Learning. In Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI), 2014.
    Details     BibTeX     Download: (unavailable)
  15. Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, and Ann Nowé. Multi-Objectivization in Reinforcement Learning. Technical Report AI-TR-13-354, AI Lab, Vrije Universiteit Brussel, 2013.
    Details     BibTeX     Download: (unavailable)

Multi-Agent

  1. Ivomar Brito Soares, Yann-Michaël De Hauwere, Kris Januarius, Tim Brys, Thierry Salvant, and Ann Nowé. Departure MANagement with a Reinforcement Learning Approach: Re- specting CFMU Slots. In Proceedings of the IEEE International Conference on Intelligent Trans- portation Systems (ITSC), 2015.
    Details     BibTeX     Download: (unavailable)
  2. Tim Brys, Tong T. Pham, and Matthew E. Taylor. Distributed learning and multi-objectivity in traffic light control. Connection Science, 26(1):65–83, Taylor & Francis, 2014.
    Details     BibTeX     Download: [pdf] (891.2kB )  
  3. Tong T. Pham, Tim Brys, and Matthew E. Taylor. Learning Coordinated Traffic Light Control. In Proceedings of the Adaptive and Learning Agents workshop at AAMAS, May 2013.
    ALA-13
    Details     BibTeX     Download: [pdf] (471.3kB )  
  4. Tim Brys, Yann-Michaël De Hauwere, Ann Nowé, and Peter Vrancx. Local Coordination in Online Distributed Constraint Optimization Problems. In Massimo Cossentino, Michael Kaisers, Karl Tuyls, and Gerhard Weiss, editors, Multi-Agent Systems, Lecture Notes in Computer Science, pp. 31–47, Springer Berlin / Heidelberg, 2012.
    Details     BibTeX     Download: [pdf] (840.6kB )  
  5. Tim Brys, Yann-Michaël De Hauwere, Ann Nowé, and Peter Vrancx. Local Coordination in Online Distributed Constraint Optimization Problems. In The 9th European Workshop on Multi-Agent Systems, Maastricht, The Netherlands, 2011.
    Details     BibTeX     Download: (unavailable)

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