Markel Sanz Ausin

Ausin, M. S., Abdelshiheed, M., Barnes, T., & Chi, M. (2023). A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics. https://doi.org/10.1007/978-3-031-36336-8_93

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2022, November 28). The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00312-3

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2021, August 16). Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction. International Journal of Artificial Intelligence in Education, Vol. 8. https://doi.org/10.1007/s40593-021-00269-9

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2021, January 1). Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-78292-4_29

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2020, January 1). Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies. Lecture Notes in Computer Science, pp. 472–485. https://doi.org/10.1007/978-3-030-52237-7_38

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2019, January 1). Hierarchical Reinforcement Learning for Pedagogical Policy Induction. Lecture Notes in Computer Science, Vol. 11625, pp. 544–556. https://doi.org/10.1007/978-3-030-23204-7_45