no code implementations • 23 Sep 2024 • Seif Gad, Sherif Abdelfattah, Ghodai Abdelrahman
Yet, achieving this objective is a non-trivial task as it involves modeling the knowledge state across multiple knowledge components (KCs) while considering their temporal and relational dynamics during the learning process.
no code implementations • 18 Aug 2023 • Sherif Abdelfattah, Adrian Brown, Pushi Zhang
This paper addresses these limitations by proposing an agent design that mainly depends on pixel-based state observations while exploring the environment conditioned on a user's preference specified by demonstration trajectories.
no code implementations • 18 Aug 2023 • Sherif Abdelfattah, Kathryn Kasmarik, Jiankun Hu
We propose a novel multi-objective reinforcement learning algorithm that can robustly evolve a convex coverage set of policies in an online manner in non-stationary environments.
no code implementations • 18 Aug 2023 • Sherif Abdelfattah, Kathryn Merrick, Jiankun Hu
In this work, intrinsically motivated reinforcement learning has been successfully deployed to evolve generic skill sets for learning hierarchical policies to solve multi-objective Markov decision processes.
no code implementations • 19 Aug 2022 • Ghodai Abdelrahman, Sherif Abdelfattah, Qing Wang, Yu Lin
This is known as the \emph{Knowledge Tracing} problem in the literature.