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Results: 5
Number of items: 5
  • Open Access
    Wöhlke, J. G. (2024). Reinforcement learning and planning for autonomous agent navigation: With a focus on sparse reward settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2023). Learning Hierarchical Planning-Based Policies from Offline Data. In D. Koutra, C. Plant, M. Gomes Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings (Vol. IV, pp. 489–505). (Lecture Notes in Computer Science; Vol. 14172), ( Lecture Notes in Artificial Intelligence ). Springer. https://doi.org/10.1007/978-3-031-43421-1_29
  • Open Access
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2022). Value Refinement Network (VRN). In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: IJCAI 2022, Vienna, Austria, 23-29 July 2022 (pp. 3558-3565). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/494
  • 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
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1503-1511). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398934
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