title: A Zero-Shot Language Agent for Computer Control with Structured
    Reflection
summary: 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.
paper_authors._id: 652cbbb468f1d7d1d29a24c7
paper_authors.name: Tao Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-10-17T07:31:04.350Z
paper_authors._id: 5f8e07f35d083370c711f5d2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1603143637927-noauth.jpeg
paper_authors.fullname: Tao Li
paper_authors.type: user
paper_authors.user: tli8hf
paper_authors._id: 652cbbb468f1d7d1d29a24c8
paper_authors.name: Gang Li
paper_authors._id: 652cbbb468f1d7d1d29a24c9
paper_authors.name: Zhiwei Deng
paper_authors._id: 652cbbb468f1d7d1d29a24ca
paper_authors.name: Bryan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T15:47:51.793Z
paper_authors._id: 6215a70a6f827861d125fe2e
paper_authors.avatarUrl: /avatars/c84b243e02ca2b71af079c8220c5fe44.svg
paper_authors.fullname: Bryan Wang
paper_authors.type: user
paper_authors.user: bw0514
paper_authors._id: 652cbbb468f1d7d1d29a24cb
paper_authors.name: Yang Li
paper_title: A Zero-Shot Language Agent for Computer Control with Structured
    Reflection
paper_summary: 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.
paper_ai_keywords: zero-shot agent self-reflection structured thought management executable actions partially observed environment