An Overview of Natural Language State Representation for Reinforcement Learning

July 19, 2020 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: An Overview of Natural Language State Representation for Reinforcement Learning"

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Authors Brielen Madureira, David Schlangen arXiv ID 2007.09774 Category cs.CL: Computation & Language Citations 10 Venue arXiv.org Last Checked 3 days ago
Abstract
A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or be natural language itself. This survey outlines the strategies used in the literature to build natural language state representations. We appeal for more linguistically interpretable and grounded representations, careful justification of design decisions and evaluation of the effectiveness of different approaches.
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