# GridWorld Classroom Simulation
## Summary
This application simulates a classroom environment as a grid world. The agent navigates the classroom, interacting with students, teachers, and objects, with both numeric and language-based state representations.
## Data Source
The environment is based on a custom classroom grid, with student and teacher features provided in CSV and Python data files.
## elsciRL App Options
### Local Config
| Config Name | Config Code | Description |
|:-------------:|:-----------:|:--------------------------------------------------------------------------:|
| Classroom_A | classroom_A | Standard classroom layout with students, teachers, and objects. |
### Adapters
| Adapter Name | Adapter Code | Description | Output type |
|:--------------------:|:------------:|:---------------------------------------------------------------------------:|:-----------:|
| Default Numeric | default | Encodes the agent's position and classroom state as a tensor. | $tensor$ |
| Classroom Language | language | Describes the classroom state in natural language using student features. | $tensor$ |
| LLM Adapter | llm | Uses an LLM to generate a language description of the classroom state. | $tensor$ |
## Citation
```bibtex
@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}
}
```