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Results: 56
Number of items: 56
  • Open Access
    Long, A., Blair, A., & van Hoof, H. (2022). Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation. In K. Sycara, V. Honavar, & M. Spaan (Eds.), Proceedings of the 36th AAAI Conference on Artificial Intelligence: AAAI-22 : virtual conference, Vancouver, Canada, February 22-March 1, 2022 (Vol. 7, pp. 7620-7627). AAAI Press. https://doi.org/10.1609/aaai.v36i7.20728
  • Open Access
    Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Wang, S., Sporrel, K., van Hoof, H., Simons, M., de Boer, R. D. D., Ettema, D., Nibbeling, N., Deutekom, M., & Kröse, B. (2021). Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study. International Journal of Environmental Research and Public Health, 18(11), Article 6059. https://doi.org/10.3390/ijerph18116059
  • Open Access
    Wang, S., Zhang, C., Kröse, B., & van Hoof, H. (2021). Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. Journal of medical systems, 45(12), Article 102. https://doi.org/10.1007/s10916-021-01773-0
  • Open Access
    Van Der Pol, E., Worrall, D., Van Hoof, H., Oliehoek, F., & Welling, M. (2021). MDP homomorphic networks: Group symmetries in reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 6, pp. 4199-4210). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2be5f9c2e3620eb73c2972d7552b6cb5-Abstract.html
  • Open Access
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2021). Hierarchies of Planning and Reinforcement Learning for Robot Navigation. In 2021 IEEE International Conference on Robotics and Automation (ICRA 2021): May 31-June 4, 2021, Xi'an, China (pp. 10682-10688). IEEE. https://doi.org/10.48550/arXiv.2109.11178, https://doi.org/10.1109/ICRA48506.2021.9561151
  • Open Access
    Zhang, Y., & van Hoof, H. (2021). Deep Coherent Exploration For Continuous Control. Proceedings of Machine Learning Research, 139, 12567-12577. https://proceedings.mlr.press/v139/zhang21t.html
  • Open Access
    Bakker, T., Van Hoof, H., & Welling, M. (2021). Experimental design for MRI by greedy policy search. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 23, pp. 18954-18966). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/daed210307f1dbc6f1dd9551408d999f-Abstract.html
  • Open Access
    Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 190–199). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412252
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