Works (5)
Updated: July 5th, 2023 15:05
2022 article
The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way
Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2022, November 28). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.
author keywords: Deep reinforcement learning; Pedagogical policy; Explanation
TL;DR:
The results suggest that pairing simple explanations with the Batch DRL policy with explanations can be an important and effective technique for applying RL to real-life, human-centric tasks.
(via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: November 29, 2022
2021 article
Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction
Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2021, August 16). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 8.
author keywords: Hierarchical reinforcement learning; Decision granularity; Pedagogical policy
TL;DR:
An offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework is proposed and applied to induce a hierarchical pedagogical policy that makes adaptive, effective decisions at both the problem and step levels.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: August 23, 2021
2021 article
Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 356–368.
author keywords: Pedagogical agent; Credit assignment problem; Deep reinforcement learning
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Sources: ORCID, Web Of Science
Added: June 16, 2021
2020 chapter
Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies
In Lecture Notes in Computer Science (pp. 472–485).
author keywords: Deep reinforcement learning; Pedagogical policy; Explanation
TL;DR:
The results suggest that pairing simple explanations with induced RL policies can be an important and effective technique for applying RL to real-life human-centric tasks.
(via Semantic Scholar)
link.springer.com (publisher PDF)
Source: ORCID
Added: September 14, 2020
2019 article
Hierarchical Reinforcement Learning for Pedagogical Policy Induction
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, Vol. 11625, pp. 544–556.
author keywords: Hierarchical Reinforcement Learning; Pedagogical policies
TL;DR:
This paper proposes and applies an offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels and shows that the HRL policy is significantly more effective than a Deep Q-Network induced policy and a random yet reasonable baseline policy.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions
(OpenAlex)
Sources: Web Of Science, ORCID
Added: December 2, 2019
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