A NetHack Learning Environment Language Wrapper for Autonomous Agents

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.

Keywords

• autonomous agents
• generalization
• language models
• language representations
• nethack
• reinforcement learning
• transfer learning