AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun Yuchen Zhuang Lingkai Kong Bo Dai Chao Zhang
Large language models (LLMs) have recently demonstrated the potential in
acting as autonomous agents for sequential decision-making tasks. However, most
existing methods either take actions greedily without planning or rely on
static plans that are not adaptable to environmental feedback. Consequently,
the sequential decision-making performance of LLM agents degenerates with
problem complexity and plan horizons increase. We propose a closed-loop
approach, AdaPlanner, which allows the LLM agent to refine its self-generated
plan adaptively in response to environmental feedback. In AdaPlanner, the LLM
agent adaptively refines its plan from feedback with both in-plan and
out-of-plan refinement strategies. To mitigate hallucination, we develop a
code-style LLM prompt structure that facilitates plan generation across a
variety of tasks, environments, and agent capabilities. Furthermore, we propose
a skill discovery mechanism that leverages successful plans as few-shot
exemplars, enabling the agent to plan and refine with fewer task
demonstrations. Our experiments in the ALFWorld and MiniWoB++ environments
demonstrate that AdaPlanner outperforms state-of-the-art baselines by 3.73% and
4.11% while utilizing 2x and 600x fewer samples, respectively.