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Preference-conditioned Pixel-based AI Agent For Game Testing
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.
A Robust Policy Bootstrapping Algorithm for Multi-objective Reinforcement Learning in Non-stationary Environments
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.
Intrinsically Motivated Hierarchical Policy Learning in Multi-objective Markov Decision Processes
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.
DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation
This is known as the \emph{Knowledge Tracing} problem in the literature.
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