M. Suau, M.T.J. Spaan, F.A. Oliehoek (2023), Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL, In European Reinforcement Learning Workshop.

Q. Yang, M.T.J. Spaan (2023), CEM: Constrained Entropy Maximization for Task-Agnostic Safe Exploration, In The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23).

Qisong Yang, Thiago D. Simão, Nils Jansen, Simon H. Tindemans, Matthijs T.J. Spaan (2023), Reinforcement Learning by Guided Safe Exploration, Kobi Gal, Kobi Gal, Ann Nowe, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu (Eds.), In ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings p.2858 - 2865.

Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli, Matthijs T.J. Spaan (2023), Scalable Safe Policy Improvement via Monte Carlo Tree Search, In Proceedings of Machine Learning Research p.3732-3756.

Danial Kamran, Thiago D. Simão, Qisong Yang, Canmanie T. Ponnambalam, Johannes Fischer, Matthijs T.J. Spaan, Martin Lauer (2022), A Modern Perspective on Safe Automated Driving for Different Traffic Dynamics using Constrained Reinforcement Learning, In Proceedings of the IEEE International Conference on Intelligent Transportation Systems p.4017-4023, IEEE .

Sebastian Junges, Matthijs T.J. Spaan (2022), Abstraction-Refinement for Hierarchical Probabilistic Models, Sharon Shoham, Yakir Vizel (Eds.), In Computer Aided Verification - 34th International Conference, CAV 2022, Proceedings p.102-123, Springer.

Johan Los, Frederik Schulte, Matthijs T.J. Spaan, Rudy R. Negenborn (2022), An Auction-Based Multi-Agent System for the Pickup and Delivery Problem with Autonomous Vehicles and Alternative Locations, Michael Freitag, Aseem Kinra, Herbert Kotzab, Nicole Megow (Eds.), In Dynamics in Logistics p.244-260, Springer.

Canmanie Ponnambalam, Danial Kamran, Thiago D. Simão, Frans A. Oliehoek, Matthijs T.J. Spaan (2022), Back to the Future: Solving Hidden Parameter MDPs with Hindsight.

M. Suau, J. He, Mustafa Mert Çelikok, M.T.J. Spaan, F.A. Oliehoek (2022), Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho , A. Oh (Eds.), In Advances in Neural Information Processing Systems, Morgan Kaufmann Publishers.

Miguel Suau, Jinke He, Matthijs T.J. Spaan, Frans A. Oliehoek (2022), Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, S. Sabato (Eds.), In Proceedings of the 39th International Conference on Machine Learning Volume 162 p.20604-20624, PMLR.