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        <namePart type="given">Prithviraj</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)</title>
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            <namePart type="given">Dmitry</namePart>
            <namePart type="family">Ustalov</namePart>
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            <namePart type="given">Swapna</namePart>
            <namePart type="family">Somasundaran</namePart>
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            <namePart type="given">Peter</namePart>
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    <abstract>Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.</abstract>
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%0 Conference Proceedings
%T Transfer in Deep Reinforcement Learning Using Knowledge Graphs
%A Ammanabrolu, Prithviraj
%A Riedl, Mark
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F ammanabrolu-riedl-2019-transfer
%X Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.
%R 10.18653/v1/D19-5301
%U https://aclanthology.org/D19-5301
%U https://doi.org/10.18653/v1/D19-5301
%P 1-10

Markdown (Informal)

[Transfer in Deep Reinforcement Learning Using Knowledge Graphs](https://aclanthology.org/D19-5301) (Ammanabrolu & Riedl, TextGraphs 2019)

• Transfer in Deep Reinforcement Learning Using Knowledge Graphs (Ammanabrolu & Riedl, TextGraphs 2019)

ACL

• Prithviraj Ammanabrolu and Mark Riedl. 2019. Transfer in Deep Reinforcement Learning Using Knowledge Graphs. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 1–10, Hong Kong. Association for Computational Linguistics.