# Playing Text-Based Games with Common Sense

arXiv: https://arxiv.org/abs/2012.02757 · HTML: https://mlsanity.com/paper/2012.02757/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 21 of 21)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling](https://mlsanity.com/paper/2301.12050/) (2023)
- [Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence](https://mlsanity.com/paper/2210.07109/) (2022)
- [Aligning to Social Norms and Values in Interactive Narratives](https://mlsanity.com/paper/2205.01975/) (2022)
- [Learning Knowledge Graph-based World Models of Textual Environments](https://mlsanity.com/paper/2106.09608/) (2021)
- [LARP: Language-Agent Role Play for Open-World Games](https://mlsanity.com/paper/2312.17653/) (2023)
- [AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems](https://mlsanity.com/paper/2402.08995/) (2024)
- [Monte Carlo Planning with Large Language Model for Text-Based Game Agents](https://mlsanity.com/paper/2504.16855/) (2025)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior](https://mlsanity.com/paper/2104.09469/) (2021)
- [Unbounded: A Generative Infinite Game of Character Life Simulation](https://mlsanity.com/paper/2410.18975/) (2024)
- [Modeling Worlds in Text](https://mlsanity.com/paper/2106.09578/) (2021)
- [Situated Dialogue Learning through Procedural Environment Generation](https://mlsanity.com/paper/2110.03262/) (2021)
- [Story Shaping: Teaching Agents Human-like Behavior with Stories](https://mlsanity.com/paper/2301.10107/) (2023)
- [Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents](https://mlsanity.com/paper/2106.05387/) (2021)
- [kogito: A Commonsense Knowledge Inference Toolkit](https://mlsanity.com/paper/2211.08451/) (2022)
- [Learning to Query Internet Text for Informing Reinforcement Learning Agents](https://mlsanity.com/paper/2205.13079/) (2022)
- [The Odyssey of the Fittest: Can Agents Survive and Still Be Good?](https://mlsanity.com/paper/2502.05442/) (2025)
- [DiffG-RL: Leveraging Difference between State and Common Sense](https://mlsanity.com/paper/2211.16002/) (2022)
- [Generalizability of experimental studies](https://mlsanity.com/paper/2406.17374/) (2024)
- [Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators](https://mlsanity.com/paper/2601.16866/) (2026)

## Abstract (arXiv)

Text based games are simulations in which an agent interacts with the world purely through natural language. They typically consist of a number of puzzles interspersed with interactions with common everyday objects and locations. Deep reinforcement learning agents can learn to solve these puzzles. However, the everyday interactions with the environment, while trivial for human players, present as additional puzzles to agents. We explore two techniques for incorporating commonsense knowledge into agents. Inferring possibly hidden aspects of the world state with either a commonsense inference model COMET, or a language model BERT. Biasing an agents exploration according to common patterns recognized by a language model. We test our technique in the 9to05 game, which is an extreme version of a text based game that requires numerous interactions with common, everyday objects in common, everyday scenarios. We conclude that agents that augment their beliefs about the world state with commonsense inferences are more robust to observational errors and omissions of common elements from text descriptions.