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.
- 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
, abstract, bibtex)
- 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
, abstract, bibtex)
- 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. (abstract, bibtex)
- 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
, abstract, bibtex)
- 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
, abstract, bibtex)
- 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
, abstract, bibtex, tech.
report (pdf))
- 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
paper, abstract, bibtex, tech.
report (pdf))
- 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
, abstract, bibtex)
- 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
, abstract, bibtex)
- 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
, abstract, bibtex)
- 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
, abstract, bibtex, tech.
report (pdf))
- 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, tech.
report (pdf))
- 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
, abstract, bibtex, tech.
report (pdf))
- 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, abstract, bibtex, tech.
report (pdf))
- 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
, abstract, bibtex, tech.
report (pdf))
- 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
, abstract, bibtex, tech.
report (pdf))
- 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
, abstract, bibtex, tech.
report (pdf)) [see also: erratum, software toolbox]
- 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
, abstract, bibtex) [see also: related paper]
- 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
, abstract, bibtex, tech.
report (pdf)) [see also: original paper]
- 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)
- 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
, abstract, bibtex, tech.
report (pdf))
- 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
, abstract, bibtex, tech.
report (pdf))
- 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, abstract, bibtex, tech.
report (pdf))
- 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.
report (pdf))
- 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.
report (pdf)) [see also: erratum]
- 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
, abstract, bibtex, tech.
report (pdf)) [see also: original paper]
- 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.
report (pdf))
- 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 paper, abstract, bibtex, tech.
report (pdf))
- 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 paper, abstract, bibtex, tech.
report (pdf))
- 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
version, abstract, bibtex, tech.
report (pdf))
- 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
paper, abstract, bibtex, tech.
report (pdf))
- 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. (abstract,
bibtex, tech.
report (pdf))
- 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. (abstract,
bibtex, tech.
report (pdf))
- 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
paper, abstract, bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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
paper, abstract, bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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
version, abstract, bibtex, tech.
report (pdf))
- 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 link, bibtex)
- 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
version, abstract, bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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
version, abstract, bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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.
report (pdf))
- 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
paper, abstract, bibtex, tech.
report (pdf))
- 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.
(abstract, bibtex, tech.
report (pdf))
- 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 paper,
abstract, bibtex, tech.
report (pdf))
- 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
version, abstract, bibtex, tech.
report (pdf))
- 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.
(abstract, bibtex, tech.
report (pdf))
- 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. (abstract,
bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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. (abstract,
bibtex, tech.
report (pdf))
- 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. (abstract, bibtex, report
(pdf))
- 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. (abstract, bibtex, report
(pdf))
- 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. (abstract, bibtex, tech.
report (pdf))
- 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.
report (pdf))
- 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.
report (pdf))
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This page is maintained by Bart De
Schutter.
Last update: February 3, 2025.