Empowering Large Language Model Agents through Action Learning
Haiteng Zhao Chang Ma Guoyin Wang Jing Su Lingpeng Kong Jingjing Xu Zhi-Hong Deng Hongxia Yang
Large Language Model (LLM) Agents have recently garnered increasing interest
yet they are limited in their ability to learn from trial and error, a key
element of intelligent behavior. In this work, we argue that the capacity to
learn new actions from experience is fundamental to the advancement of learning
in LLM agents. While humans naturally expand their action spaces and develop
skills through experiential learning, LLM agents typically operate within fixed
action spaces, limiting their potential for growth. To address these
challenges, our study explores open-action learning for language agents. We
introduce a framework LearnAct with an iterative learning strategy to create
and improve actions in the form of Python functions. In each iteration, LLM
revises and updates the currently available actions based on the errors
identified in unsuccessful training tasks, thereby enhancing action
effectiveness. Our experimental evaluations across Robotic Planning and
Alfworld environments reveal that after learning on a few training task
instances, our approach to open-action learning markedly improves agent
performance for the type of task (by 32 percent in AlfWorld compared to
ReAct+Reflexion, for instance) highlighting the importance of experiential
action learning in the development of more intelligent LLM agents.