Bridging the Connectrion Between Deep Learning and Stochastic Optimal Control
Author(s)
Chen, Tianrong
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Abstract
Generative models have gained significant popularity in recent years, and Stochastic Optimal Control soc has also advanced rapidly in parallel. This thesis addresses the problem of understanding dynamical generative models from the perspective of Stochastic Optimal Control, thereby providing insights from the well-established Stochastic Optimal Control theory. Additionally, it explores the challenges of high-dimensional Stochastic Optimal Control by leveraging deep learning techniques. Through this dual approach, the research aims to enhance the theoretical understanding and practical application of generative models and Stochastic Optimal Control in complex, high-dimensional environments.
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Date
2024-08-22
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Dissertation