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Fall 2022
The motivation to incorporate planning, temporal abstraction and value function approximation in reinforcement learning (RL) algorithms is to reduce the amount of interaction with the environment needed to learn a near-optimal policy. Although each of these concepts has been under intense...
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Fall 2024
Despite the brain's inherent ability to continually learn, biological insights are rarely applied to continual reinforcement learning (RL). This thesis addresses this gap by examining four under-investigated biologically-inspired modifications within the context of continual RL: energy...
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Investigating Feature Importance In Educational Data, Towards Handling Data Missingness in Classification Tasks
DownloadSpring 2024
The problem of missing data is unavoidable in many research fields, especially in education where data can be missing for justifiable reasons. Missing data causes bias in analysis, and traditional methods like complete case analysis and single imputation are suboptimal yet typically used to...
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Fall 2023
Recurrent Neural Networks (RNNs) are typically used to learn representations in partially observable environments. Unfortunately, training RNNs is known to be difficult, and the difficulty increases for agents who learn online and continually interact with the environment. Two common strategies...