Aug 16, 2021 · In this work, we first investigate the impact of decision granularity on student learning, and then, apply hierarchical reinforcement learning ( ...
– Our analysis of the HRL policy's decision-making sheds some light on how to leverage the impact of decision granularity to improve student learning. 2 The ...
In an empirical classroom study, our results showed that the HRL policy is significantly more effective than a Deep Q-Network (DQN) induced step-level policy ...
In interactive e-learning environments such as Intelligent Tutoring Systems, pedagogical decisions can be made at different levels of granularity.
Jan 1, 2022 · Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical ...
In this paper, we propose and apply an offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical ...
In this work, we pro- pose and apply an offline hierarchical reinforcement learning. (HRL) framework to induce a pedagogical policy that makes decisions at two ...
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In this paper, we propose and apply an offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical ...
This paper proposes and applies an offline Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical ...
A unified batch hierarchical reinforcement learning framework for pedagogical policy induction with deep bisimulation metrics.