Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, current LfD frameworks are not capable of fast adaptation to heterogeneous human demonstrations nor the large-scale deployment in ubiquitous robotics applications. In this paper, we propose a novel LfD framework, Fast Lifelong Adaptive Inverse Reinforcement learning (FLAIR). Our approach (1) leverages learned strategies to construct policy mixtures for fast adaptation to new demonstrations, allowing for quick end-user personalization, (2) distills common knowledge across demonstrations, achieving accurate task inference; and (3) expands its model only when needed in lifelong deployments, maintaining a concise set of prototypical strategies that can approximate all behaviors via policy mixtures. We empirically validate that FLAIR achieves adaptability (i.e., the robot adapts to heterogeneous, user-specific task preferences), efficiency (i.e., the robot achieves sample-efficient adaptation), and scalability (i.e., the model grows sublinearly with the number of demonstrations while maintaining high performance). FLAIR surpasses benchmarks across three control tasks with an average 57% improvement in policy returns and an average 78% fewer episodes required for demonstration modeling using policy mixtures. Finally, we demonstrate the success of FLAIR in a table tennis task and find users rate FLAIR as having higher task (p<.05) and personalization (p<.05) performance

A Strategic Plan to Thread Genomics Competencies into Undergraduate Curriculum

Problem: Genomics in undergraduate nursing education has experienced slow adoption in the United States. Various approaches have been proposed but do not address barriers to successful implementation. Methods: A strategic plan was developed to increase the amount of genetics and genomic content in the curriculum of an undergraduate nursing program. A gap analysis was performed on the curriculum revealing a paucity of content. A SWOT analysis informed the strategic plan, which included a faculty education program using the ANA/ISONG’s Essentials of Genetic and Genomic Nursing: Competencies, Curricula Guidelines and Outcome Indicators (2nd ed.) (2009) as a foundation. Results: Faculty developed 18 activities and evaluation methods by which students could acquire essential genetics and genomics competencies that can be threaded into the curriculum. Faculty interest in learning about genetics and genomics increased from 47.8% to 81.8% of attendees. Confidence in knowledge of genetics and genomics increased as well. A team approach to the idea of a faculty champion was identified. Implications: This project demonstrated that a strategic plan tailored to a school, involving faculty members in the process, and working as a team to develop curriculum threads is a successful approach to increasing genomics curriculum threads for use in undergraduate curriculum. This project also demonstrated that a team approach increased faculty confidence of knowledge and interest in genetics and genomics and fostered the idea that a team of faculty champions may be superior to one individual in such a role

Statistical Machine Learning Methodology for Individualized Treatment Rule Estimation in Precision Medicine

Precision medicine aims to deliver optimal, individualized treatments for patients by accounting for their unique characteristics. With a foundation in reinforcement learning, decision theory, and causal inference, the field of precision medicine has seen many advancements in recent years. Significant focus has been placed on creating algorithms to estimate individualized treatment rules (ITRs), which map from patient covariates to the space of available treatments with the goal of maximizing patient outcome. In Chapter 1, we extend ITR estimation methodology in the scenario where variance of the outcome is heterogeneous with respect to treatment and covariates. Accordingly, we propose Stabilized Direct Learning (SD-Learning), which utilizes heteroscedasticity in the error term through a residual reweighting framework that models residual variance via flexible machine learning algorithms such as XGBoost and random forests. We also develop an internal cross-validation scheme which determines the best residual model among competing models. Further, we extend this methodology to multi-arm treatment scenarios. In Chapter 2, we develop ITR estimation methodology for situations where clinical decision-making involves balancing multiple outcomes of interest. Our proposed framework estimates an ITR which maximizes a combination of the multiple clinical outcomes, accounting for the fact that patients may ascribe importance to outcomes differently (utility heterogeneity). This approach employs inverse reinforcement learning (IRL) techniques through an expert-augmentation solution, whereby physicians provide input to guide the utility estimation and ITR learning processes. In Chapter 3, we apply an end-to-end precision medicine workflow to novel data from older adults with Type 1 Diabetes in order to understand the heterogeneous treatment effects of continuous glucose monitoring (CGM) and develop an interpretable ITR to reveal patients for which CGM confers a major safety benefit. The results from this analysis elucidate the demographic and clinical markers which moderate CGM's success, provide the basis for using diagnostic CGM to inform therapeutic CGM decisions, and serve to augment clinical decision-making. Finally, in Chapter 4, as a future research direction, we propose a deep autoencoder framework which simultaneously performs feature selection and ITR optimization, contributing to methodology built for direct consumption of unstructured, high-dimensional data in the precision medicine pipeline.Doctor of Philosoph

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.Comment: 29 pages, 20 figure

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi