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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[details]
Wöhlke, J., Schmitt, F., & Hoof, H. V. (2022). Value Refinement Network (VRN). In International Joint Conference on Artificial Intelligence IJCAI. https://doi.org/10.24963/ijcai.2022/494
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[details]
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]. [details]
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