자료유형  

 학위논문

Control Number  

0017162492

International Standard Book Number  

9798383057247

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Zentner, K. R.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Southern California., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

111 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Sukhatme, Gaurav S.

Dissertation Note  

Thesis (Ph.D.)--University of Southern California, 2024.

Summary, Etc.  

요약The past few years have seen an explosion of interest in using machine learning to make robots capable of learning a diverse set of tasks. These robots use Reinforcement Learning to learn detailed sub-second interactions, but consequently require large amounts of data for each task. In this thesis we explore how Reinforcement Learning can be combined with Transfer Learning to re-use data across tasks. We begin by reviewing the start of Multi-Task and Meta RL and describe the motivations for using Transfer Learning. Then, we describe a basic framework for using Transfer Learning to efficiently learn multiple tasks, and show how it requires predicting how effectively transfer can be performed across tasks. Next, we present a simple rule, based in information theory, for predicting the effectiveness of Cross-Task Transfer. We discuss the theoretical implications of that rule, and show various quantitative evaluations of it. Then, we show two directions of work making use of our insights to perform efficient Transfer Reinforcement Learning. The first of these directions uses Cross-Task Co-Learning and Plan Conditioned Behavioral Cloning to share skill representations produced by a Large Language Model, and it able to learn many tasks from a single demonstration each in a simulated environment. The second of these directions uses Two-Phase KL Penalization to enforce a (potentially off-policy) trust region. These advances in Transfer RL may enable robots to be used in a wider range of applications, and may also inform applying Transfer RL outside of robotics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Robotics.

Subject Added Entry-Topical Term  

Information technology.

Index Term-Uncontrolled  

Imitation learning

Index Term-Uncontrolled  

Large Language Models

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Reinforcement learning

Index Term-Uncontrolled  

Transfer learning

Added Entry-Corporate Name  

University of Southern California Computer Science

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:657165