  We show that GPT-4's reasoning and planning capabilities extend to the 1993
first-person shooter Doom. This large language model (LLM) is able to run and
play the game with only a few instructions, plus a textual
description--generated by the model itself from screenshots--about the state of
the game being observed. We find that GPT-4 can play the game to a passable
degree: it is able to manipulate doors, combat enemies, and perform pathing.
More complex prompting strategies involving multiple model calls provide better
results. While further work is required to enable the LLM to play the game as
well as its classical, reinforcement learning-based counterparts, we note that
GPT-4 required no training, leaning instead on its own reasoning and
observational capabilities. We hope our work pushes the boundaries on
intelligent, LLM-based agents in video games. We conclude by discussing the
ethical implications of our work.
