# Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.02386 · HTML: https://mlsanity.com/paper/2010.02386/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 27 of 27)

- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Survey on Large Language Model-Based Game Agents](https://mlsanity.com/paper/2404.02039/) (2024)
- [AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents](https://mlsanity.com/paper/2407.04363/) (2024)
- [LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models](https://mlsanity.com/paper/2311.18232/) (2023)
- [Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions](https://mlsanity.com/paper/2304.02868/) (2023)
- [Multi-Stage Episodic Control for Strategic Exploration in Text Games](https://mlsanity.com/paper/2201.01251/) (2022)
- [Monte Carlo Planning with Large Language Model for Text-Based Game Agents](https://mlsanity.com/paper/2504.16855/) (2025)
- [Pre-trained Language Models as Prior Knowledge for Playing Text-based Games](https://mlsanity.com/paper/2107.08408/) (2021)
- [Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents](https://mlsanity.com/paper/2103.13552/) (2021)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [TVShowGuess: Character Comprehension in Stories as Speaker Guessing](https://mlsanity.com/paper/2204.07721/) (2022)
- [A Survey of Text Games for Reinforcement Learning informed by Natural Language](https://mlsanity.com/paper/2109.09478/) (2021)
- [TextWorldExpress: Simulating Text Games at One Million Steps Per Second](https://mlsanity.com/paper/2208.01174/) (2022)
- [Generalization in Text-based Games via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2109.09968/) (2021)
- [Perceiving the World: Question-guided Reinforcement Learning for Text-based Games](https://mlsanity.com/paper/2204.09597/) (2022)
- [Case-based Reasoning for Better Generalization in Textual Reinforcement Learning](https://mlsanity.com/paper/2110.08470/) (2021)
- [Conceptual Reinforcement Learning for Language-Conditioned Tasks](https://mlsanity.com/paper/2303.05069/) (2023)
- [Temporal Knowledge-Graph Memory in a Partially Observable Environment](https://mlsanity.com/paper/2408.05861/) (2024)
- [Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games](https://mlsanity.com/paper/2505.12439/) (2025)
- [Language Decision Transformers with Exponential Tilt for Interactive Text Environments](https://mlsanity.com/paper/2302.05507/) (2023)
- [Revisiting the Roles of "Text" in Text Games](https://mlsanity.com/paper/2210.08384/) (2022)
- [Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning](https://mlsanity.com/paper/2302.10720/) (2023)
- [Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games](https://mlsanity.com/paper/2304.07258/) (2023)
- [Dual-Scale World Models for LLM Agents Towards Hard-Exploration Problems](https://mlsanity.com/paper/2509.24116/) (2025)
- [Feudal Reinforcement Learning by Reading Manuals](https://mlsanity.com/paper/2110.06477/) (2021)
- [JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions](https://mlsanity.com/paper/2210.15456/) (2022)

## Abstract (arXiv)

Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. Our source code is available at: https://github.com/XiaoxiaoGuo/rcdqn.