# ScriptWorld: Text Based Environment For Learning Procedural Knowledge

arXiv: https://arxiv.org/abs/2307.03906 · HTML: https://mlsanity.com/paper/2307.03906/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 54 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 25 of 25)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [The NetHack Learning Environment](https://mlsanity.com/paper/2006.13760/) (2020)
- [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)
- [Toward Understanding Catastrophic Forgetting in Continual Learning](https://mlsanity.com/paper/1908.01091/) (2019)
- [Learning Dynamic Belief Graphs to Generalize on Text-Based Games](https://mlsanity.com/paper/2002.09127/) (2020)
- [Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines](https://mlsanity.com/paper/2010.03790/) (2020)
- [MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge](https://mlsanity.com/paper/1803.05223/) (2018)
- [Pre-trained Language Models as Prior Knowledge for Playing Text-based Games](https://mlsanity.com/paper/2107.08408/) (2021)
- [Interactive Language Learning by Question Answering](https://mlsanity.com/paper/1908.10909/) (2019)
- [What do Large Language Models Learn about Scripts?](https://mlsanity.com/paper/2112.13834/) (2021)
- [LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games](https://mlsanity.com/paper/1909.01646/) (2019)
- [Using Sentence-Level LSTM Language Models for Script Inference](https://mlsanity.com/paper/1604.02993/) (2016)
- [InScript: Narrative texts annotated with script information](https://mlsanity.com/paper/1703.05260/) (2017)
- [Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games](https://mlsanity.com/paper/1908.04777/) (2019)
- [Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games](https://mlsanity.com/paper/2009.11896/) (2020)
- [Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction](https://mlsanity.com/paper/1702.03121/) (2017)
- [Transfer in Deep Reinforcement Learning using Knowledge Graphs](https://mlsanity.com/paper/1908.06556/) (2019)

## Built on by (most cited first, 2 of 2)

- [EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association](https://mlsanity.com/paper/2505.15196/) (2025)
- [ScheduleMe: Multi-Agent Calendar Assistant](https://mlsanity.com/paper/2509.25693/) (2025)

## Abstract (arXiv)

Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents. Existing text-based environments often rely on fictional situations and characters to create a gaming framework and are far from real-world scenarios. In this paper, we introduce ScriptWorld: a text-based environment for teaching agents about real-world daily chores and hence imparting commonsense knowledge. To the best of our knowledge, it is the first interactive text-based gaming framework that consists of daily real-world human activities designed using scripts dataset. We provide gaming environments for 10 daily activities and perform a detailed analysis of the proposed environment. We develop RL-based baseline models/agents to play the games in Scriptworld. To understand the role of language models in such environments, we leverage features obtained from pre-trained language models in the RL agents. Our experiments show that prior knowledge obtained from a pre-trained language model helps to solve real-world text-based gaming environments. We release the environment via Github: https://github.com/Exploration-Lab/ScriptWorld