# Language is Power: Representing States Using Natural Language in Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.02789 · HTML: https://mlsanity.com/paper/1910.02789/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Semantic Exploration from Language Abstractions and Pretrained Representations](https://mlsanity.com/paper/2204.05080/) (2022)
- [Improving Intrinsic Exploration with Language Abstractions](https://mlsanity.com/paper/2202.08938/) (2022)
- [Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.06760/) (2022)
- [LanGWM: Language Grounded World Model](https://mlsanity.com/paper/2311.17593/) (2023)
- [Representation-Driven Reinforcement Learning](https://mlsanity.com/paper/2305.19922/) (2023)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Separating Decision Time from Decision Quality in the Real-Time Gap of Distilled Deciders: Evidence from a Game and a Conveyor Simulator](https://mlsanity.com/paper/2610.04810/) (2026)

## Abstract (arXiv)

Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks. However, as the dimensionality of the task increases, reinforcement learning methods tend to struggle. To overcome this, we explore methods for representing the semantic information embedded in the state. While previous methods focused on information in its raw form (e.g., raw visual input), we propose to represent the state using natural language. Language can represent complex scenarios and concepts, making it a favorable candidate for representation. Empirical evidence, within the domain of ViZDoom, suggests that natural language based agents are more robust, converge faster and perform better than vision based agents, showing the benefit of using natural language representations for reinforcement learning.