An Overview of Natural Language State Representation for Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
6 papers.
- 2022Dynamic Dialogue Policy for Continual Reinforcement Learningcited by 12
- 2021Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentimentcited by 2
- 2023Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teacherscited by 2
- 2024A Survey on Complex Tasks for Goal-Directed Interactive Agentscited by 2
- 2025Graph-Enhanced Policy Optimization in LLM Agent Trainingcited by 2
- 2021Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environmentscited by 1
- 2025Graph-Enhanced Policy Optimization in LLM Agent Training30 Oct 2025
- 2024A Survey on Complex Tasks for Goal-Directed Interactive Agents27 Sep 2024
- 2023Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers22 May 2023
- 2022Dynamic Dialogue Policy for Continual Reinforcement Learning12 Apr 2022
- 2021Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentiment15 Jun 2021
- 2021Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments11 May 2021