# Gym: FrozenLake ## Summary This application provides an interface to the classic FrozenLake environment from Gymnasium. The agent must navigate a slippery grid to reach the goal, avoiding holes. ## Data Source Based on the [FrozenLake](https://gymnasium.farama.org/environments/toy_text/frozen_lake/) environment from Gymnasium. ## elsciRL App Options ### Local Config | Config Name | Config Code | Description | |:-----------:|:-----------:|:--------------------------------------------------------------------------:| | 4x4 | 4x4 | Standard 4x4 FrozenLake grid. | ### Adapters | Adapter Name | Adapter Code | Description | Output type | |:-----------------:|:------------:|:---------------------------------------------------------------------------:|:-----------:| | Numeric | numeric | Encodes the grid state as a tensor of indices. | $tensor$ | | Language | language | Describes the grid state in natural language. | $tensor$ | | LLM Adapter | llm | Uses an LLM to generate a language description of the grid state. | $tensor$ | ## Citation ```bibtex @article{towers2024gymnasium, title={Gymnasium: A Standard Interface for Reinforcement Learning Environments}, author={Towers, Mark and Kwiatkowski, Ariel and Terry, Jordan and Balis, John U and De Cola, Gianluca and Deleu, Tristan and Goul{\~a}o, Manuel and Kallinteris, Andreas and Krimmel, Markus and KG, Arjun and others}, journal={arXiv preprint arXiv:2407.17032}, year={2024} } @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} } ```