A Zero-Shot Language Agent for Computer Control with Structured
  Reflection
Authors: name: Tao Li, name: Gang Li, name: Zhiwei Deng, name: Bryan Wang, name: Yang Li

Large language models (LLMs) have shown increasing capacity at planning and
executing a high-level goal in a live computer environment (e.g. MiniWoB++). To
perform a task, recent works often require a model to learn from trace examples
of the task via either supervised learning or few/many-shot prompting. Without
these trace examples, it remains a challenge how an agent can autonomously
learn and improve its control on a computer, which limits the ability of an
agent to perform a new task. We approach this problem with a zero-shot agent
that requires no given expert traces. Our agent plans for executable actions on
a partially observed environment, and iteratively progresses a task by
identifying and learning from its mistakes via self-reflection and structured
thought management. On the easy tasks of MiniWoB++, we show that our zero-shot
agent often outperforms recent SoTAs, with more efficient reasoning. For tasks
with more complexity, our reflective agent performs on par with prior best
models, even though previous works had the advantages of accessing expert
traces or additional screen information.