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Ensembling Diverse Policies Improves Generalization of Deep Reinforcement Learning Algorithms to Environmental Changes in Continuous Control Tasks
DownloadFall 2023
Deep Reinforcement Learning (DRL) algorithms have shown great success in solving continuous control tasks. However, they often struggle to generalize to changes in the environment. Although retraining may help policies adapt to changes, it may be quite costly in some environments. Ensemble...
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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...