# Maze Simulation
## Summary
This application recreates the maze problems from "[Natural Language Reinforcement Learning](https://arxiv.org/abs/2402.07157)" (2023), adapted for the elsciRL framework. Agents must navigate mazes using both numeric and language-based state representations.
## Data Source
Based on code from [waterhorse1/Natural-language-RL](https://github.com/waterhorse1/Natural-language-RL), adapted for elsciRL.
## elsciRL App Options
### Local Config
| Config Name | Config Code | Description |
|:-----------:|:-----------:|:--------------------------------------------------------------------------:|
| umaze | umaze | U-shaped maze configuration. |
| double_t | double_t | Double T-shaped maze configuration. |
| medium | medium | Medium-sized maze. |
| large | large | Large maze configuration. |
| random | random | Randomly generated maze. |
### Adapters
| Adapter Name | Adapter Code | Description | Output type |
|:-----------------:|:------------:|:---------------------------------------------------------------------------:|:-----------:|
| Language | language | Describes the maze state in natural language. | $tensor$ |
| LLM Adapter | llm | Uses an LLM to generate a language description of the maze state. | $tensor$ |
## Citation
```bibtex
@misc{nlrl,
title={Natural Language Reinforcement Learning},
author={Xidong Feng and Ziyu Wan and Haotian Fu and Bo Liu and Mengyue Yang and Girish A. Koushik and Zhiyuan Hu and Ying Wen and Jun Wang},
year={2024},
eprint={2411.14251},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2411.14251},
}
```