A nethack learning environment language wrapper for autonomous agents
- Authors: Goodger, Nikolaj , Vamplew, Peter , Foale, Cameron , Dazeley, Richard
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Open Research Software Vol. 11, no. (2023), p.
- Full Text:
- Reviewed:
- Description: 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/.
Language representations for generalization in reinforcement learning
- Authors: Goodger, Nikolaj , Vamplew, Peter , Foale, Cameron , Dazeley, Richard
- Date: 2021
- Type: Text , Conference paper
- Relation: 13th Asian Conference on Machine Learning, Virtual, 17-19 November 2021, Proceedings of The 13th Asian Conference on Machine Learning Vol. 157, p. 390-405
- Full Text:
- Reviewed:
- Description: The choice of state and action representation in Reinforcement Learning (RL) has a significant effect on agent performance for the training task. But its relationship with generalization to new tasks is under-explored. One approach to improving generalization investigated here is the use of language as a representation. We compare vector-states and discreteactions to language representations. We find the agents using language representations generalize better and could solve tasks with more entities, new entities, and more complexity than seen in the training task. We attribute this to the compositionality of language