Towards Better Efficiency and Generalization in Imitation Learning: A Causal Perspective

Simple item page
dc.contributor.advisorShi, Javen Qinfeng
dc.contributor.advisorAbbasnejad, Ehsan
dc.contributor.authorJabri, Mohamed Khalil
dc.contributor.schoolSchool of Computer and Mathematical Sciences
dc.date.issued2024
dc.description.abstractImitation Learning, also known as Learning from Demonstrations, has emerged as a practical alternative to reinforcement learning, mitigating the intricate challenges associated with reward engineering in the latter. However, imitation learning agents often face limitations that hinder their effectiveness in realistic scenarios. Sample efficiency and generalization pose notable challenges among these limitations. Concurrently, there has been an increasing acknowledgment of causality’s significance in improving learning-based approaches, resulting in its recent prominence within the machine learning community. This dissertation explores the potential of causality-inspired approaches to address the aforementioned challenges in imitation learning through two distinct contributions. The first contribution introduces a novel method to enhance goal-conditioned imitation learning using Structural Causal Models (SCMs) and counterfactual data. We leverage SCMs as a formalism to understand the inherent causal relationships between different variables governing expert behavior. This enables the generation of counterfactual data, which we utilize to learn improved reward functions with reduced data, thereby enhancing the agent’s efficiency. The second contribution focuses on identifying causal features that remain consistent across different environments. Unlike many works on domain generalization, our method is equally applicable to Reinforcement Learning and Imitation Learning, eschewing the need for domain supervision and remaining agnostic to data modality, rendering it broadly applicable. Through empirical evaluation, this dissertation establishes the efficacy of causalityinspired approaches in advancing imitation learning capabilities. The proposed methodologies not only contribute to overcoming some fundamental limitations of existing imitation learning algorithms but also provide valuable insights into the broader application of causality in machine learning.
dc.description.dissertationThesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 2024en
dc.identifier.urihttps://hdl.handle.net/2440/145038
dc.language.isoen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.subjectReinforcement Learning
dc.subjectImitation Learning
dc.subjectCausality
dc.subjectCounterfactual Reasoning
dc.subjectGeneralization
dc.titleTowards Better Efficiency and Generalization in Imitation Learning: A Causal Perspective
dc.typeThesisen

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Jabri2024_PhD.pdf
Size:
1.96 MB
Format:
Adobe Portable Document Format
Download

Collections

Research Theses