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Importance Sampling to Identify Empirically Valid Policies and their Critical Decisions.

, , , , and . EDM (Workshops), volume 2592 of CEUR Workshop Proceedings, page 69-78. CEUR-WS.org, (2019)

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Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction., , , , and . Int. J. Artif. Intell. Educ., 32 (2): 454-500 (2022)Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning., , , , and . EDM, International Educational Data Mining Society (IEDMS), (2019)Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract)., , , , and . IJCAI, page 4691-4695. ijcai.org, (2020)Sister Conferences Best Papers.Automated Assessment of Computer Science Competencies from Student Programs with Gaussian Process Regression., , , , , , , and . EDM, International Educational Data Mining Society, (2020)Hierarchical Reinforcement Learning for Pedagogical Policy Induction., , , , and . AIED (1), volume 11625 of Lecture Notes in Computer Science, page 544-556. Springer, (2019)Importance Sampling to Identify Empirically Valid Policies and their Critical Decisions., , , , and . EDM (Workshops), volume 2592 of CEUR Workshop Proceedings, page 69-78. CEUR-WS.org, (2019)Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System., , , and . EDM, International Educational Data Mining Society (IEDMS), (2019)Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts., , , , and . IJCAI, page 1974-1980. ijcai.org, (2019)InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem., , , , and . IEEE BigData, page 1337-1348. IEEE, (2021)Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies., , , , and . UMAP, page 284-292. ACM, (2020)