New Frameworks for Structured Policy Learning

Citation

Le, Hoang Minh (2020) New Frameworks for Structured Policy Learning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/rz4w-k233. https://resolver.caltech.edu/CaltechTHESIS:06092020-121556493

Abstract

Sequential decision making applications are playing an increasingly important role in everyday life. Research interest in machine learning approaches to sequential decision making has surged thanks to recent empirical successes of reinforcement learning and imitation learning techniques, partly fueled by recent advances in deep learning-based function approximation. However in many real-world sequential decision making applications, relying purely on black box policy learning is often insufficient, due to practical requirements of data efficiency, interpretability, safety guarantees, etc. These challenges collectively make it difficult for many existing policy learning methods to find success in realistic applications.

In this dissertation, we present recent advances in structured policy learning, which are new machine learning frameworks that integrate policy learning with principled notions of domain knowledge, which spans value-based, policy-based, and model-based structures. Our framework takes flexible reduction-style approaches that can integrate structure with reinforcement learning, imitation learning and robust control techniques. In addition to methodological advances, we demonstrate several successful applications of the new policy learning frameworks.

Thesis Files