![]() ![]() | FELTEN, F., TALBI, E.-G., & DANOY, G. (26 February 2024). Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework. Journal of Artificial Intelligence Research, 79, 679-723. doi:10.1613/jair.1.15702 ![]() |
![]() ![]() | FELTEN, F.* , Alegre, L. N.* , Nowé, A., L. C. Bazzan, A., TALBI, E.-G., DANOY, G., & C. da Silva, B. (2024). A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. In A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. United States: Curran Associates. ![]() * These authors have contributed equally to this work. |
![]() ![]() | FELTEN, F., GAREEV, D., TALBI, E.-G., & DANOY, G. (October 2023). Hyperparameter Optimization for Multi-Objective Reinforcement Learning [Paper presentation]. Multi-Objective Decision Making Workshop (MoDeM), Krakow, Poland. doi:10.48550/arXiv.2310.16487 ![]() |
![]() ![]() | Alegre, L. N., FELTEN, F., TALBI, E.-G., DANOY, G., Nowé, A., Bazzan, A., & da Silva, B. (November 2022). MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments [Paper presentation]. BNAIC/BeNeLearn 2022, Mechelen, Belgium. |
![]() ![]() | FELTEN, F., Danoy, G., TALBI, E.-G., & BOUVRY, P. (2022). Metaheuristics-based Exploration Strategies for Multi-Objective Reinforcement Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (pp. 662--673). Online Streaming, Unknown/unspecified: SCITEPRESS - Science and Technology Publications. doi:10.5220/0010989100003116 ![]() |
![]() ![]() | FELTEN, F., TALBI, E.-G., & DANOY, G. (2022). MORL/D: Multi-Objective Reinforcement Learning based on Decomposition. In International Conference in Optimization and Learning (OLA2022). doi:10.5220/0010989100003116 ![]() |
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![]() ![]() | DUFLO, G., DANOY, G., TALBI, E.-G., & BOUVRY, P. (2022). Learning to Optimise a Swarm of UAVs. Applied Sciences, 12 (19 9587). doi:10.3390/app12199587 ![]() |
![]() ![]() | DUFLO, G., DANOY, G., TALBI, E.-G., & BOUVRY, P. (2021). A Q-Learning Based Hyper-Heuristic for Generating Efficient UAV Swarming Behaviours. In Intelligent Information and Database Systems - 13th Asian Conference ACIIDS 2021, Phuket, Thailand, April 7-10, 2021, Proceedings (pp. 768--781). Springer. doi:10.1007/978-3-030-73280-6\_61 ![]() |
![]() ![]() | DUFLO, G., DANOY, G., TALBI, E.-G., & BOUVRY, P. (2020). Automating the Design of Efficient Distributed Behaviours for a Swarm of UAVs. In IEEE Symposium Series on Computational Intelligence, Canberra 1-4 December 2020 (pp. 489-496). IEEE. doi:10.1109/SSCI47803.2020.9308355 ![]() |
![]() ![]() | DUFLO, G., DANOY, G., TALBI, E.-G., & BOUVRY, P. (2020). A Q-Learning Hyper-Heuristic for UAV Swarming [Paper presentation]. OLA'2020 Int. Conference on Optimization and Learning, Cadiz, Spain. |
![]() ![]() | DUFLO, G., DANOY, G., TALBI, E.-G., & BOUVRY, P. (2020). Automated design of efficient swarming behaviours: a Q-learning hyper-heuristic approach. In GECCO '20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020 (pp. 227--228). ACM. doi:10.1145/3377929.3390026 ![]() |