  Large Language Models (LLMs) have shown great success as high-level planners
for zero-shot game-playing agents. However, these agents are primarily
evaluated on Minecraft, where long-term planning is relatively straightforward.
In contrast, agents tested in dynamic robot environments face limitations due
to simplistic environments with only a few objects and interactions. To fill
this gap in the literature, we present NetPlay, the first LLM-powered zero-shot
agent for the challenging roguelike NetHack. NetHack is a particularly
challenging environment due to its diverse set of items and monsters, complex
interactions, and many ways to die.
  NetPlay uses an architecture designed for dynamic robot environments,
modified for NetHack. Like previous approaches, it prompts the LLM to choose
from predefined skills and tracks past interactions to enhance decision-making.
Given NetHack's unpredictable nature, NetPlay detects important game events to
interrupt running skills, enabling it to react to unforeseen circumstances.
While NetPlay demonstrates considerable flexibility and proficiency in
interacting with NetHack's mechanics, it struggles with ambiguous task
descriptions and a lack of explicit feedback. Our findings demonstrate that
NetPlay performs best with detailed context information, indicating the
necessity for dynamic methods in supplying context information for complex
games such as NetHack.
