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.



  1. S. Mallick, F. Airaldi, A. Dabiri, C. Sun, and B. De Schutter, "Reinforcement learning-based model predictive control for greenhouse climate control," Smart Agricultural Technology, vol. 10, p. 100751, Mar. 2025.  (online paper  [open access]abstractbibtex)

  2. K. He, S. Shi, T. van den Boom, and B. De Schutter, "Approximate dynamic programming for constrained piecewise affine systems with stability and safety guarantees," IEEE Transactions on Systems, Man and Cybernetics: Systems, 2025. Early access.  (online paper  [open access]abstractbibtex)

  3. K. He, S. Shi, T. van den Boom, and B. De Schutter, "Efficient and safe learning-based control of piecewise affine systems using optimization-free safety filters," Proceedings of the 63rd IEEE Conference on Decision and Control, Milan, Italy, Dec. 2024.  (abstractbibtex)

  4. D. Sun, A. Jamshidnejad, and B. De Schutter, "Adaptive parameterized model predictive control based on reinforcement learning: A synthesis framework," Engineering Applications of Artificial Intelligence, vol. 136-B, p. 109009, Oct. 2024.  (online paper  [open access]abstractbibtex)

  5. S. Mallick, F. Airaldi, A. Dabiri, and B. De Schutter, "Multi-agent reinforcement learning via distributed MPC as a function approximator," Automatica, vol. 167, p. 111803, Sept. 2024.  (online paper  [open access]abstractbibtex)

  6. H. Zhang, X. Liu, D. Sun, A. Dabiri, and B. De Schutter, "Integrated reinforcement learning and optimization for railway timetable rescheduling," Proceedings of the 17th IFAC Symposium on Control in Transportation Systems (CTS 2024), Ayia Napa, Cyprus, pp. 310-315, July 2024.  (online paper  [open access]abstractbibtextech. report (pdf))

  7. A. Athrey, O. Mazhar, M. Guo, B. De Schutter, and S. Shi, "Regret analysis of learning-based linear quadratic Gaussian control with additive exploration," Proceedings of the 2024 European Control Conference, Stockholm, Sweden, pp. 1795-1801, June 2024.  (online paperabstractbibtextech. report (pdf))

  8. 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  [open access]abstractbibtex)

  9. 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, vol. 25, no. 7, pp. 6756-6769, 2024.  (online paper  [open access]abstractbibtex)

  10. 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  [open access]abstractbibtex)

  11. 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  [open access]abstractbibtextech. report (pdf))

  12. 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 paperabstractbibtextech. report (pdf))

  13. 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  [open access]abstractbibtextech. report (pdf))

  14. 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 paperabstractbibtextech. report (pdf))

  15. A. Ilioudi, A. Dabiri, B.J. Wolf, 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  [open access]abstractbibtextech. report (pdf))

  16. 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  [open access]abstractbibtextech. report (pdf))

  17. 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  [open access]abstractbibtextech. report (pdf))   [see also: erratumsoftware toolbox]

  18. 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.  (online version  [open access]abstractbibtex)   [see also: related paper]

  19. 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.  (online version  [open access]abstractbibtextech. report (pdf))   [see also: original paper]

  20. 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  [open access]bibtex)

  21. 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  [open access]abstractbibtextech. report (pdf))

  22. 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  [open access]abstractbibtextech. report (pdf))

  23. 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 paperabstractbibtextech. report (pdf))

  24. 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 paperabstractbibtextech. report (pdf))

  25. 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  [open access]abstractbibtextech. report (pdf))   [see also: erratum]

  26. 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  [open access]abstractbibtextech. report (pdf))   [see also: original paper]

  27. 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  [open access]abstractbibtextech. report (pdf))

  28. F. Ruelens, B.J. Claessens, S. Quaiyum, B. De Schutter, R. Babuška, 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 paperabstractbibtextech. report (pdf))

  29. F. Ruelens, B.J. Claessens, S. Vandael, B. De Schutter, R. Babuška, 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 paperabstractbibtextech. report (pdf))

  30. L. Buşoniu, A. Lazaric, M. Ghavamzadeh, R. Munos, R. Babuška, 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 versionabstractbibtextech. report (pdf))

  31. J. van Ast, R. Babuška, 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 paperabstractbibtextech. report (pdf))

  32. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  33. L. Buşoniu, R. Munos, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  34. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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 paperabstractbibtextech. report (pdf))

  35. J. van Ast, R. Babuška, 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.  (abstractbibtextech. report (pdf))

  36. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "Online least-squares policy iteration for reinforcement learning control," Proceedings of the 2010 American Control Conference, Baltimore, Maryland, pp. 486-491, June-July 2010.  (abstractbibtextech. report (pdf))

  37. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "Approximate dynamic programming with a fuzzy parameterization," Automatica, vol. 46, no. 5, pp. 804-814, May 2010.  (online paperabstractbibtextech. report (pdf))

  38. L. Buşoniu, B. De Schutter, R. Babuška, 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.  (abstractbibtextech. report (pdf))

  39. L. Buşoniu, R. Babuška, 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 versionabstractbibtextech. report (pdf))

  40. L. Buşoniu, R. Babuška, 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 linkbibtex)

  41. L. Buşoniu, B. De Schutter, and R. Babuška, "Approximate dynamic programming and reinforcement learning," in Interactive Collaborative Information Systems (R. Babuška 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 versionabstractbibtextech. report (pdf))

  42. L. Buşoniu, B. De Schutter, R. Babuška, 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.  (abstractbibtextech. report (pdf))

  43. J.M. van Ast, R. Babuška, and B. De Schutter, "Ant colony learning algorithm for optimal control," in Interactive Collaborative Information Systems (R. Babuška 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 versionabstractbibtextech. report (pdf))

  44. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  45. 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 paperabstractbibtextech. report (pdf))

  46. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "Fuzzy partition optimization for approximate fuzzy Q-iteration," Proceedings of the 17th IFAC World Congress, Seoul, Korea, pp. 5629-5634, July 2008.  (online paperabstractbibtextech. report (pdf))

  47. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  48. L. Buşoniu, R. Babuška, 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 paperabstractbibtextech. report (pdf))

  49. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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 versionabstractbibtextech. report (pdf))

  50. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  51. L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  52. L. Buşoniu, R. Babuška, 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.  (abstractbibtextech. report (pdf))

  53. L. Buşoniu, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  54. R. Babuška, L. Buşoniu, and B. De Schutter, "Reinforcement learning for multi-agent systems," Tech. report 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.  (abstractbibtexreport (pdf))

  55. L. Buşoniu, B. De Schutter, and R. Babuška, "Learning and coordination in dynamic multiagent systems," Tech. report 05-019, Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands, 98 pp., Oct. 2005.  (abstractbibtexreport (pdf))

  56. L. Buşoniu, B. De Schutter, and R. Babuška, "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.  (abstractbibtextech. report (pdf))

  57. 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 paperabstractbibtextech. report (pdf))

  58. 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.  (abstractbibtextech. report (pdf))



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This page is maintained by Bart De Schutter. Last update: February 3, 2025.