# A Survey of Reinforcement Learning Informed by Natural Language

arXiv: https://arxiv.org/abs/1906.03926 · HTML: https://mlsanity.com/paper/1906.03926/

## Key facts

- Cited by 154 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 30 of 154)

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- [LTL2Action: Generalizing LTL Instructions for Multi-Task RL](https://mlsanity.com/paper/2102.06858/) (2021)
- [Learning to Model the World with Language](https://mlsanity.com/paper/2308.01399/) (2023)
- [LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions](https://mlsanity.com/paper/2405.11106/) (2024)
- ["No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy](https://mlsanity.com/paper/2301.02555/) (2023)
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## Abstract (arXiv)

To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. Recent advances in representation learning for language make it possible to build models that acquire world knowledge from text corpora and integrate this knowledge into downstream decision making problems. We thus argue that the time is right to investigate a tight integration of natural language understanding into RL in particular. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge. Finally, we call for the development of new environments as well as further investigation into the potential uses of recent Natural Language Processing (NLP) techniques for such tasks.