Metadata-Version: 2.2
Name: textcraft
Version: 0.0.3
Summary: Textcraft environment
Author-email: Nikolai Rozanov <nikolai.rozanov@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Nikolai Rozanov, ADaPT Authors
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: gymnasium>=0.26.0
Requires-Dist: numpy>=1.24.0

# Textcraft Environment

This is the Pypi implementation and packaging of the environment Textcraft introduced in the paper ADaPT.

## Getting started:
The package comes with all the data already, so usage is super easy.

1. Installation:
```bash
pip3 install textcraft
```

2. Usage:
```python
from textcraft import TextCraft


env = TextCraft()
obs, info = env.reset(seed=42)
print(obs)
action = input("> ")
(observation, reward, terminated, truncated, info) = env.step(action)
print(observation, reward, sep="\n")
```


## Acknowledgements
We thank the authors and contributors of [ADaPT](https://allenai.github.io/adaptllm/) for their public code release. 

## Reference
Please cite StateAct:
```bibtex
@article{rozanov2024stateactstatetrackingreasoning,
      title={StateAct: State Tracking and Reasoning for Acting and Planning with Large Language Models}, 
      author={Nikolai Rozanov and Marek Rei},
      year={2024},
      eprint={2410.02810},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2410.02810}, 
}
```

ADaPT
```bibtex

@article{prasad2023adapt,
      author    = "Prasad, Archiki and Koller, Alexander and Hartmann, Mareike and Clark, Peter and Sabharwal, Ashish and Bansal, Mohit and Khot, Tushar",
      title     = "ADaPT: As-Needed Decomposition and Planning with Language Models",
      journal   = "arXiv",
      year      = "2023",}
}
```
