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Fall 2019
In this thesis, we investigate sparse representations in reinforcement learning. We begin by discussing catastrophic interference in reinforcement learning with function approximation, and empirically investigating difficulties of online reinforcement learning in both policy evaluation and...
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Towards Practical Offline Reinforcement Learning: Sample Efficient Policy Selection and Evaluation
DownloadSpring 2024
Offline reinforcement learning (RL) involves learning policies from datasets, rather than online interaction. The dissertation first investigates a critical component in offline RL: offline policy selection (OPS). Given that most offline RL algorithms require careful hyperparameter tuning, we...