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Learning Dynamic Belief Graphs to Generalize on Text-Based Games

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10432150" target="_blank" >RIV/00216208:11320/20:10432150 - isvavai.cz</a>

  • Result on the web

    <a href="https://proceedings.neurips.cc/paper/2020/hash/1fc30b9d4319760b04fab735fbfed9a9-Abstract.html" target="_blank" >https://proceedings.neurips.cc/paper/2020/hash/1fc30b9d4319760b04fab735fbfed9a9-Abstract.html</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learning Dynamic Belief Graphs to Generalize on Text-Based Games

  • Original language description

    Playing text-based games requires skills in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challenge, and prior research has largely relied on hand-crafted structured representations and heuristics. In this work, we investigate how an agent can plan and generalize in text-based games using graph-structured representations learned end-to-end from raw text. We propose a novel graph-aided transformer agent (GATA) that infers and updates latent belief graphs during planning to enable effective action selection by capturing the underlying game dynamics. GATA is trained using a combination of reinforcement and self-supervised learning. Our work demonstrates that the learned graph-based representations help agents converge to better policies than their text-only counterparts and facilitate effective generalization across game configurations. Experiments on 500+ unique games from the TextWorld suite show that our best agent outperforms text-based baselines by an average of 24.2%.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Advances in Neural Information Processing Systems 33

  • ISBN

    978-1-71382-954-6

  • ISSN

    1049-5258

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    3045-3057

  • Publisher name

    Curran Associates, Inc.

  • Place of publication

    Neuveden

  • Event location

    Virtuální

  • Event date

    Dec 6, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article