mlsanityData to 7 Oct 2026

TextWorld: A Learning Environment for Text-based Games

arXiv 1806.11532 ↗First posted 29 Jun 2018cs.LG cs.CL stat.MLcited by 304

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Cited by 102 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.

Key facts. Cited by 304 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2018.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 0; 2021: 33; 2022: 27; 2023: 52; 2024: 53; 2025: 60; 2026: 76.

304 papers.

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  11. 2021Implicit Representations of Meaning in Neural Language Modelscited by 179
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38 more
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  6. 2024BALROG: Benchmarking Agentic LLM and VLM Reasoning On Gamescited by 141
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  9. 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluationscited by 118
  10. 2024A Survey on Large Language Model-Based Game Agentscited by 106
  11. 2019Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learningcited by 100
  12. 2024AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agentscited by 100
  13. 2024DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agentscited by 89
  14. 2023MindAgent: Emergent Gaming Interactioncited by 87
  15. 2023LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Modelscited by 84
  16. 2024RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agentscited by 80
  17. 2025Agent Learning via Early Experiencecited by 79
  18. 2023Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arenacited by 78
  19. 2018Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Interventioncited by 77
  20. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
  21. 2021Chess as a Testbed for Language Model State Trackingcited by 69
  22. 2023Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorldcited by 65
  23. 2024StateFlow: Enhancing LLM Task-Solving through State-Driven Workflowscited by 65
  24. 2024SelfIE: Self-Interpretation of Large Language Model Embeddingscited by 63
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  27. 2026Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skillscited by 53
  28. 2024Can Language Models Serve as Text-Based World Simulators?cited by 50
  29. 2022Improving Intrinsic Exploration with Language Abstractionscited by 49
  30. 2023Learning to Generate Better Than Your LLMcited by 49
  31. 2023Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoningcited by 49
  32. 2023Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Modelscited by 46
  33. 2026AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applicationscited by 46
  34. 2023Entity Tracking in Language Modelscited by 44
  35. 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planningcited by 44
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  37. 2025From Word to World: Can Large Language Models be Implicit Text-based World Models?cited by 40
  38. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38

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