# TextWorldExpress ## Summary This application provides an interface to the TextWorldExpress environment, allowing RL agents to interact with text-based worlds and tasks. Supports both numeric and language-based adapters. ## Data Source Based on the [TextWorldExpress](https://github.com/cognitiveailab/TextWorldExpress) environment. ## elsciRL App Options ### Local Config | Config Name | Config Code | Description | |:-----------:|:-----------:|:--------------------------------------------------------------------------:| | CoinCollector | coin | Collect coins in a text-based world. | | CookingWorld | cooking | Complete cooking tasks in a text-based world. | ### Adapters | Adapter Name | Adapter Code | Description | Output type | |:-----------------:|:------------:|:---------------------------------------------------------------------------:|:-----------:| | Default Numeric | default | Encodes the text world state as a tensor of indices. | $tensor$ | | Language | language | Describes the text world state in natural language. | $tensor$ | | LLM Adapter | llm | Uses an LLM to generate a language description of the text world state. | $tensor$ | ## Citation ```bibtex @article{jansen2022textworldexpress, url = {https://arxiv.org/abs/2208.01174}, author = {Jansen, Peter A. and Côté, Marc-Alexandre}, title = {TextWorldExpress: Simulating Text Games at One Million Steps Per Second}, journal = {arXiv}, year = {2022}, } @phdthesis{Osborne2024, title = {Improving Real-World Reinforcement Learning by Self Completing Human Instructions on Rule Defined Language}, author = {Philip Osborne}, year = 2024, month = {August}, address = {Manchester, UK}, note = {Available at \url{https://research.manchester.ac.uk/en/studentTheses/improving-real-world-reinforcement-learning-by-self-completing-hu}}, school = {The University of Manchester}, type = {PhD thesis} } ```