Program synthesis approaches to improving generalization in reinforcement learning
Name
1193029436-MIT.pdf
Size
1.8 MB
Format
Adobe PDF
Checksum (MD5)
ae27f87979e557ee76be8ce2f9981ed1
Author(s)
Schneider, Martin Franz.
Advisor(s)
Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
To perform real-world tasks, robots need to rapidly explore and model their environment. However, existing methods either explore slowly, are data-inefficient, or need to leverage significant prior knowledge that limits their generalization. In this thesis, we explore applications of techniques from the program synthesis literature to improve the generalization and data-efficiency of reinforcement learning agents. Two complementary approaches are explored. First, we explore leveraging program synthesis techniques to meta-learn exploration strategies, and automatically synthesize new explorations strategies competitive with state of the art benchmarks. Second, we explore applying program synthesis to the problem of learning factored world models and achieve promising preliminary results. We see these results as promising examples of the potential of integrating program synthesis techniques with the rest of our modern modern reinforcement learning and robotics toolkits, increasing generalization in the process.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 63-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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