# Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

arXiv: https://arxiv.org/abs/2103.13552 · HTML: https://mlsanity.com/paper/2103.13552/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 15 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Keep CALM and Explore: Language Models for Action Generation in Text-based Games](https://mlsanity.com/paper/2010.02903/) (2020)
- [Deep Reinforcement Learning with a Natural Language Action Space](https://mlsanity.com/paper/1511.04636/) (2015)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://mlsanity.com/paper/1506.08941/) (2015)
- [Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning](https://mlsanity.com/paper/2010.02386/) (2020)
- [NAIL: A General Interactive Fiction Agent](https://mlsanity.com/paper/1902.04259/) (2019)
- [What can you do with a rock? Affordance extraction via word embeddings](https://mlsanity.com/paper/1703.03429/) (2017)
- [How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents](https://mlsanity.com/paper/2002.08795/) (2020)
- [Deriving Commonsense Inference Tasks from Interactive Fictions](https://mlsanity.com/paper/2010.09788/) (2020)

## Built on by (most cited first, 20 of 20)

- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [ScienceWorld: Is your Agent Smarter than a 5th Grader?](https://mlsanity.com/paper/2203.07540/) (2022)
- [Pre-Trained Language Models for Interactive Decision-Making](https://mlsanity.com/paper/2202.01771/) (2022)
- [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023)
- [LARP: Language-Agent Role Play for Open-World Games](https://mlsanity.com/paper/2312.17653/) (2023)
- [Multi-Stage Episodic Control for Strategic Exploration in Text Games](https://mlsanity.com/paper/2201.01251/) (2022)
- [Pre-trained Language Models as Prior Knowledge for Playing Text-based Games](https://mlsanity.com/paper/2107.08408/) (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)
- [TextWorldExpress: Simulating Text Games at One Million Steps Per Second](https://mlsanity.com/paper/2208.01174/) (2022)
- [Behavior Cloned Transformers are Neurosymbolic Reasoners](https://mlsanity.com/paper/2210.07382/) (2022)
- [Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games](https://mlsanity.com/paper/2505.12439/) (2025)
- [Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games](https://mlsanity.com/paper/2311.07687/) (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)
- [An Analysis of Deep Reinforcement Learning Agents for Text-based Games](https://mlsanity.com/paper/2209.04105/) (2022)
- [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)
- [On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning](https://mlsanity.com/paper/2404.10174/) (2024)

## Abstract (arXiv)

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.