<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="golchha-etal-2024-language">
    <titleInfo>
        <title>Language Guided Exploration for RL Agents in Text Environments</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hitesh</namePart>
        <namePart type="family">Golchha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sahil</namePart>
        <namePart type="family">Yerawar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dhruvesh</namePart>
        <namePart type="family">Patel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Soham</namePart>
        <namePart type="family">Dan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Keerthiram</namePart>
        <namePart type="family">Murugesan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: NAACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Duh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Mexico City, Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like tabula rasa reinforcement learning (RL) agents. Large Language Models (LLMs), with a wealth of world knowledge, can help RL agents learn quickly and adapt to distribution shifts. In this work, we introduce Language Guided Exploration (LGE) framework, which uses a pre-trained language model (called GUIDE ) to provide decision-level guidance to an RL agent (called EXPLORER ). We observe that on ScienceWorld (Wang et al., 2022), a challenging text environment, LGE outperforms vanilla RL agents significantly and also outperforms other sophisticated methods like Behaviour Cloning and Text Decision Transformer.</abstract>
    <identifier type="citekey">golchha-etal-2024-language</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-naacl.7</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>93</start>
            <end>102</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Language Guided Exploration for RL Agents in Text Environments
%A Golchha, Hitesh
%A Yerawar, Sahil
%A Patel, Dhruvesh
%A Dan, Soham
%A Murugesan, Keerthiram
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F golchha-etal-2024-language
%X Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like tabula rasa reinforcement learning (RL) agents. Large Language Models (LLMs), with a wealth of world knowledge, can help RL agents learn quickly and adapt to distribution shifts. In this work, we introduce Language Guided Exploration (LGE) framework, which uses a pre-trained language model (called GUIDE ) to provide decision-level guidance to an RL agent (called EXPLORER ). We observe that on ScienceWorld (Wang et al., 2022), a challenging text environment, LGE outperforms vanilla RL agents significantly and also outperforms other sophisticated methods like Behaviour Cloning and Text Decision Transformer.
%U https://aclanthology.org/2024.findings-naacl.7
%P 93-102

Markdown (Informal)

[Language Guided Exploration for RL Agents in Text Environments](https://aclanthology.org/2024.findings-naacl.7) (Golchha et al., Findings 2024)

• Language Guided Exploration for RL Agents in Text Environments (Golchha et al., Findings 2024)

ACL

• Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, and Keerthiram Murugesan. 2024. Language Guided Exploration for RL Agents in Text Environments. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 93–102, Mexico City, Mexico. Association for Computational Linguistics.