- Title
- A nethack learning environment language wrapper for autonomous agents
- Creator
- Goodger, Nikolaj; Vamplew, Peter; Foale, Cameron; Dazeley, Richard
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/195074
- Identifier
- vital:18470
- Identifier
-
https://doi.org/10.5334/JORS.444
- Identifier
- ISSN:2049-9647 (ISSN)
- Abstract
- This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment. © 2023 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
- Publisher
- Ubiquity Press
- Relation
- Journal of Open Research Software Vol. 11, no. (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 The Author(s)
- Rights
- Open Access
- Subject
- 46 Information and computing sciences; Autonomous agents; Generalization; Language models; Language representations; NetHack; Reinforcement learning; Transfer learning
- Full Text
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