Publications - Learning and learning-based methods

Bart De Schutter

Note: If the pdf file (pdf) of the technical report that corresponds to a given publication is available, then this is indicated at the end of the entry for that publication. More information on the pdf file format can be found here.


  1. K. He, S. Shi, T. van den Boom, and B. De Schutter, "Approximate dynamic programming for constrained linear systems: A piecewise quadratic approximation approach," Automatica, vol. 160, p. 111456, Feb. 2024.  (online paper,  bibtex)

  2. D. Sun, A. Jamshidnejad, and B. De Schutter, "A novel framework combining MPC and deep reinforcement learning with application to freeway traffic control," IEEE Transactions on Intelligent Transportation Systems, 2024.  (online paper,  bibtex)

  3. F. Airaldi, B. De Schutter, and A. Dabiri, "Learning safety in model-based reinforcement learning using MPC and Gaussian processes," Proceedings of the 22nd IFAC World Congress, Yokohama, Japan, pp. 5759-5764, July 2023.  (online paper,  bibtex)

  4. D. Sun, A. Jamshidnejad, and B. De Schutter, "Adaptive parameterized control for coordinated traffic management using reinforcement learning," Proceedings of the 22nd IFAC World Congress, Yokohama, Japan, pp. 5463-5468, July 2023.  (online paper,  bibtex)

  5. A. Ilioudi, B.J. Wolf, A. Dabiri, and B. De Schutter, "Towards establishing an automated selection framework for underwater image enhancement methods," Proceedings of the OCEANS 2023, Limerick, Ireland, June 2023.  (online paper,  abstract,  bibtex)

  6. W. Phusakulkajorn, A. Núñez, H. Wang, A. Jamshidi, A. Zoeteman, B. Ripke, R. Dollevoet, B. De Schutter, and Z. Li, "Artificial intelligence in railway infrastructure: Current research, challenges, and future opportunities," Intelligent Transportation Infrastructure, vol. 2, 2023. Paper liad016.  (online paper,  bibtex)

  7. W. Remmerswaal, D. Sun, A. Jamshidnejad, and B. De Schutter, "Combined MPC and reinforcement learning for traffic signal control in urban traffic networks," Proceedings of the 2022 26th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, pp. 432-439, Oct. 2022.  (online paper,  bibtex)

  8. A. Ilioudi, B. Wolf, A. Dabiri, and B. De Schutter, "Deep learning for object detection and segmentation in videos: Towards an integration with domain knowledge," IEEE Access, vol. 10, pp. 34562-34576, 2022.  (online paper,  bibtex)

  9. J. Lago, G. Suryanarayana, E. Sogancioglu, and B. De Schutter, "Optimal control strategies for seasonal thermal energy storage systems with market interaction," IEEE Transactions on Control Systems Technology, vol. 29, no. 5, pp. 1891-1906, Sept. 2021.  (online paper,  bibtex)

  10. J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, vol. 293, July 2021. Article 116983.  (online paper,  bibtex)

  11. J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "EPFTOOLBOX: The first open-access PYTHON library for driving research in electricity price forecasting (EPF)," WORMS Software (WORking papers in Management Science Software) WORMS/C/21/01, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, Wroclaw, Poland, 2021.  (bibtex)

  12. J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "Erratum to "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark" [Appl. Energy 293 (2021) 116983]," WORking papers in Management Science (WORMS) WORMS/21/12, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, Wroclaw, Poland, 2021.  (bibtex)

  13. D. Masti, T. Pippia, A. Bemporad, and B. De Schutter, "Learning approximate semi-explicit hybrid MPC with an application to microgrids," Proceedings of the 21st IFAC World Congress, Virtual conference, pp. 5207-5212, July 2020.  (online paper,  bibtex)

  14. N. Sapountzoglou, J. Lago, B. De Schutter, and B. Raison, "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, vol. 276, 2020. Article 115299.  (online paper,  bibtex)

  15. J. Lago, E. Sogancioglu, G. Suryanarayana, F. De Ridder, and B. De Schutter, "Building day-ahead bidding functions for seasonal storage systems: A reinforcement learning approach," Proceedings of the IFAC Workshop on Control of Smart Grid and Renewable Energy Systems (CSGRES 2019), Jeju, Republic of Korea, pp. 488-493, June 2019.  (online paper,  bibtex)

  16. J. Lago, K. De Brabandere, F. De Ridder, and B. De Schutter, "A generalized model for short-term forecasting of solar irradiance," Proceedings of the 57th IEEE Conference on Decision and Control, Miami Beach, Florida, pp. 3165-3170, Dec. 2018.  (online paper,  bibtex)

  17. J. Lago, K. De Brabandere, F. De Ridder, and B. De Schutter, "Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data," Solar Energy, vol. 173, pp. 566-577, Oct. 2018.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  18. J. Lago, F. De Ridder, and B. De Schutter, "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, vol. 221, pp. 386-405, July 2018.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  19. J. Lago, F. De Ridder, and B. De Schutter, "Erratum to "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms" [Appl. Energy 221(2018) 386–405]," Applied Energy, vol. 229, p. 1286, 2018.  (online version,  bibtex)

  20. J. Lago, F. De Ridder, P. Vrancx, and B. De Schutter, "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, vol. 211, pp. 890-903, 2018.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  21. F. Ruelens, B.J. Claessens, S. Quaiyum, B. De Schutter, R. Babuska, and R. Belmans, "Reinforcement learning applied to an electric water heater: From theory to practice," IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3792-3800, 2018.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  22. F. Ruelens, B.J. Claessens, S. Vandael, B. De Schutter, R. Babuska, and R. Belmans, "Residential demand response of thermostatically controlled loads using batch reinforcement learning," IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2149-2159, Sept. 2017.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  23. L. Busoniu, A. Lazaric, M. Ghavamzadeh, R. Munos, R. Babuska, and B. De Schutter, "Least-squares methods for policy iteration," in Reinforcement Learning: State-Of-The-Art (M. Wiering and M. van Otterlo, eds.), vol. 12 of Adaptation, Learning, and Optimization, Heidelberg, Germany: Springer, ISBN 978-3-642-27644-6, pp. 75-109, 2012.  (online version,  abstract,  bibtex,  tech. rep. (pdf))

  24. J. van Ast, R. Babuska, and B. De Schutter, "Convergence analysis of ant colony learning," Proceedings of the 18th IFAC World Congress, Milan, Italy, pp. 14693-14698, Aug.-Sept. 2011.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  25. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Approximate reinforcement learning: An overview," Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), Paris, France, pp. 1-8, Apr. 2011.  (abstract,  bibtex,  tech. rep. (pdf))

  26. L. Busoniu, R. Munos, B. De Schutter, and R. Babuska, "Optimistic planning for sparsely stochastic systems," Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), Paris, France, pp. 48-55, Apr. 2011.  (abstract,  bibtex,  tech. rep. (pdf))

  27. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Cross-entropy optimization of control policies with adaptive basis functions," IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 41, no. 1, pp. 196-209, Feb. 2011.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  28. J. van Ast, R. Babuska, and B. De Schutter, "Generalized pheromone update for ant colony learning in continuous state spaces," Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, pp. 2617-2624, July 2010.  (abstract,  bibtex,  tech. rep. (pdf))

  29. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Online least-squares policy iteration for reinforcement learning control," Proceedings of the 2010 American Control Conference, Baltimore, Maryland, pp. 486-491, June-July 2010.  (abstract,  bibtex,  tech. rep. (pdf))

  30. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Approximate dynamic programming with a fuzzy parameterization," Automatica, vol. 46, no. 5, pp. 804-814, May 2010.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  31. L. Busoniu, B. De Schutter, R. Babuska, and D. Ernst, "Using prior knowledge to accelerate online least-squares policy iteration," Proceedings of the 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2010), Cluj-Napoca, Romania, 6 pp., May 2010. Paper A-S2-1/3005.  (abstract,  bibtex,  tech. rep. (pdf))

  32. L. Busoniu, R. Babuska, and B. De Schutter, "Multi-agent reinforcement learning: An overview," Chapter 7 in Innovations in Multi-Agent Systems and Applications - 1 (D. Srinivasan and L.C. Jain, eds.), vol. 310 of Studies in Computational Intelligence, Berlin, Germany: Springer, pp. 183-221, 2010.  (online version,  abstract,  bibtex,  tech. rep. (pdf))

  33. L. Busoniu, R. Babuska, B. De Schutter, and D. Ernst, Reinforcement Learning and Dynamic Programming Using Function Approximators. Boca Raton, Florida: CRC Press, ISBN 978-1-4398-2108-4, 270 pp., 2010.  (online link,  bibtex)

  34. L. Busoniu, B. De Schutter, and R. Babuska, "Approximate dynamic programming and reinforcement learning," in Interactive Collaborative Information Systems (R. Babuska and F.C.A. Groen, eds.), vol. 281 of Studies in Computational Intelligence, Berlin, Germany: Springer, ISBN 978-3-642-11687-2, pp. 3-44, 2010.  (online version,  abstract,  bibtex,  tech. rep. (pdf))

  35. L. Busoniu, B. De Schutter, R. Babuska, and D. Ernst, "Exploiting policy knowledge in online least-squares policy iteration: An empirical study," Automation, Computers, Applied Mathematics, vol. 19, no. 4, pp. 521-529, 2010.  (abstract,  bibtex,  tech. rep. (pdf))

  36. J.M. van Ast, R. Babuska, and B. De Schutter, "Ant colony learning algorithm for optimal control," in Interactive Collaborative Information Systems (R. Babuska and F.C.A. Groen, eds.), vol. 281 of Studies in Computational Intelligence, Berlin, Germany: Springer, ISBN 978-3-642-11687-2, pp. 155-182, 2010.  (online version,  abstract,  bibtex,  tech. rep. (pdf))

  37. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Policy search with cross-entropy optimization of basis functions," Proceedings of the 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2009), Nashville, Tennessee, pp. 153-160, Mar.-Apr. 2009.  (abstract,  bibtex,  tech. rep. (pdf))

  38. Z. Lukszo, M.P.C. Weijnen, R.R. Negenborn, and B. De Schutter, "Tackling challenges in infrastructure operation and control: Cross-sectoral learning for process and infrastructure engineers," International Journal of Critical Infrastructures, vol. 5, no. 4, pp. 308-322, 2009.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  39. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Fuzzy partition optimization for approximate fuzzy Q-iteration," Proceedings of the 17th IFAC World Congress, Seoul, Korea, pp. 5629-5634, July 2008.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  40. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Consistency of fuzzy model-based reinforcement learning," Proceedings of the 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), Hong Kong, pp. 518-524, June 2008.  (abstract,  bibtex,  tech. rep. (pdf))

  41. L. Busoniu, R. Babuska, and B. De Schutter, "A comprehensive survey of multi-agent reinforcement learning," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 38, no. 2, pp. 156-172, Mar. 2008.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  42. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Continuous-state reinforcement learning with fuzzy approximation," in Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning (K. Tuyls, A. Nowé, Z. Guessoum, and D. Kudenko, eds.), vol. 4865 of Lecture Notes in Computer Science, Berlin, Germany: Springer, ISBN 978-3-540-77947-6, pp. 27-43, 2008.  (online version,  abstract,  bibtex,  tech. rep. (pdf))

  43. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Fuzzy approximation for convergent model-based reinforcement learning," Proceedings of the 2007 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007), London, UK, pp. 968-973, July 2007.  (abstract,  bibtex,  tech. rep. (pdf))

  44. L. Busoniu, D. Ernst, B. De Schutter, and R. Babuska, "Continuous-state reinforcement learning with fuzzy approximation," Proceedings of the 7th Annual Symposium on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS 2007) (K. Tuyls, S. de Jong, M. Ponsen, and K. Verbeeck, eds.), Maastricht, The Netherlands, pp. 21-35, Apr. 2007.  (abstract,  bibtex,  tech. rep. (pdf))

  45. L. Busoniu, R. Babuska, and B. De Schutter, "Multi-agent reinforcement learning: A survey," Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision (ICARCV 2006), Singapore, pp. 527-532, Dec. 2006.  (abstract,  bibtex,  tech. rep. (pdf))

  46. L. Busoniu, B. De Schutter, and R. Babuska, "Decentralized reinforcement learning control of a robotic manipulator," Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision (ICARCV 2006), Singapore, pp. 1347-1352, Dec. 2006.  (abstract,  bibtex,  tech. rep. (pdf))

  47. R. Babuska, L. Busoniu, and B. De Schutter, "Reinforcement learning for multi-agent systems," Tech. rep. 06-041, Delft Center for Systems and Control, Delft University of Technology, 7 pp., July 2006. Paper for a keynote presentation at the 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2006), Prague, Czech Republic, Sept. 2006.  (abstract,  bibtex,  report (pdf))

  48. L. Busoniu, B. De Schutter, and R. Babuska, "Learning and coordination in dynamic multiagent systems," Tech. rep. 05-019, Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands, 98 pp., Oct. 2005.  (abstract,  bibtex,  report (pdf))

  49. L. Busoniu, B. De Schutter, and R. Babuska, "Multiagent reinforcement learning with adaptive state focus," Proceedings of the 17th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2005) (K. Verbeeck, K. Tuyls, A. Nowé, B. Manderick, and B. Kuijpers, eds.), Brussels, Belgium, pp. 35-42, Oct. 2005.  (abstract,  bibtex,  tech. rep. (pdf))

  50. R.R. Negenborn, B. De Schutter, M.A. Wiering, and H. Hellendoorn, "Learning-based model predictive control for Markov decision processes," Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, pp. 354-359, July 2005.  (online paper,  abstract,  bibtex,  tech. rep. (pdf))

  51. R.R. Negenborn, B. De Schutter, M.A. Wiering, and J. Hellendoorn, "Experience-based model predictive control using reinforcement learning," Proceedings of the 8th TRAIL Congress 2004 - A World of Transport, Infrastructure and Logistics - CD-ROM, Rotterdam, The Netherlands, 18 pp., Nov. 2004.  (abstract,  bibtex,  tech. rep. (pdf))


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This page is maintained by Bart De Schutter. Last update: April 9, 2024.