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- 2model-based reinforcement learning
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Fall 2021
A common scientific challenge for putting a reinforcement learning agent into practice is how to improve sample efficiency as much as possible with limited computational or memory resources. Such available physical resources may vary in different applications. My thesis introduces some approaches...
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Fall 2018
In this thesis, we make two contributions in learning with artificial neural networks. Artificial neural networks have made great success in various challenging domains. Our first contribution is a new technique named cross-propagation that does cross-validation online. In cross-validation,...
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Spring 2024
In this dissertation, I investigate how we can exploit generic problem structure to make reinforcement learning algorithms more efficient. Generic problem structure means basic structure that exists in a wide range of problems (e.g., an action taken in the present does not influence the past), as...