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Counting to Explore and Generalize in Text-based Games

arXiv 1806.11525 ↗First posted 29 Jun 2018cs.CL cs.LGcited by 36

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Abstract (arXiv)

We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of generated text-based games of varying difficulty where the goal is to collect a coin located at the end of a chain of rooms. In contrast to previous text-based RL approaches, we observe that our agent learns policies that generalize to unseen games of greater difficulty.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 9; 2022: 9; 2023: 4; 2024: 5; 2025: 8.

36 papers.

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  1. 2022Pre-Trained Language Models for Interactive Decision-Makingcited by 220
  2. 2023Retroformer: Retrospective Large Language Agents with Policy Gradient Optimizationcited by 110
  3. 2024A Survey on Large Language Model-Based Game Agentscited by 106
  4. 2023Interactive Natural Language Processingcited by 31
  5. 2024Large Language Models Are Neurosymbolic Reasonerscited by 31
  6. 2021Neuro-Symbolic Reinforcement Learning with First-Order Logiccited by 30
  7. 2022Multi-Stage Episodic Control for Strategic Exploration in Text Gamescited by 22
  8. 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
  9. 2021Towards Socially Intelligent Agents with Mental State Transition and Human Utilitycited by 14
  10. 2021A Survey of Text Games for Reinforcement Learning informed by Natural Languagecited by 14
  11. 2024PDDLEGO: Iterative Planning in Textual Environmentscited by 14
  12. 2022TextWorldExpress: Simulating Text Games at One Million Steps Per Secondcited by 12
24 more
  1. 2022Learning to Follow Instructions in Text-Based Gamescited by 12
  2. 2021Generalization in Text-based Games via Hierarchical Reinforcement Learningcited by 11
  3. 2021Reinforcement Learning with External Knowledge by using Logical Neural Networkscited by 10
  4. 2022Perceiving the World: Question-guided Reinforcement Learning for Text-based Gamescited by 10
  5. 2022Behavior Cloned Transformers are Neurosymbolic Reasonerscited by 10
  6. 2025TALES: Text Adventure Learning Environment Suitecited by 9
  7. 2019Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spacescited by 7
  8. 2025Large Language Models Think Too Fast To Explore Effectivelycited by 7
  9. 2021LOA: Logical Optimal Actions for Text-based Interaction Gamescited by 6
  10. 2022One-Shot Learning from a Demonstration with Hierarchical Latent Languagecited by 6
  11. 2024EscapeBench: Towards Advancing Creative Intelligence of Language Model Agentscited by 5
  12. 2025A Generalist Hanabi Agentcited by 5
  13. 2023Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learningcited by 4
  14. 2023Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learningcited by 3
  15. 2025Unifying Inference-Time Planning Language Generationcited by 3
  16. 2025Language Model as Planner and Formalizer under Constraintscited by 3
  17. 2021Situated Language Learning via Interactive Narrativescited by 2
  18. 2021Interactive Machine Comprehension with Dynamic Knowledge Graphscited by 2
  19. 2025GroundAct: Can LLM Agents Ground Actions in Environmental States?cited by 2
  20. 2022An Analysis of Deep Reinforcement Learning Agents for Text-based Gamescited by 1
  21. 2024Towards Goal-Oriented Agents for Evolving Problems Observed via Conversationcited by 1
  22. 2025Iterative Formalization and Planning in Partially Observable Environmentscited by 1
  23. 2022Learning Invariable Semantical Representation from Language for Extensible Policy Generalizationcited by 0
  24. 2025ByteSized32Refactored: Towards an Extensible Interactive Text Games Corpus for LLM World Modeling and Evaluationcited by 0