I'd like to share recent work "Empowering Large Language Model Agents through Action Learning"

[zhao-ht]

Hello,

Thanks for your comprehensive and inspiring paper list! I'd like to share our recent work titled "Empowering Large Language Model Agents through Action Learning," which may be of interest to the paper list readers. The paper may be added to the Planning Section.

Paper: https://arxiv.org/abs/2402.15809
Code: https://github.com/zhao-ht/LearnAct
This work proposes the LearnAct framework, which employs an iterative learning approach to dynamically create and refine learnable actions (skills). By evaluating and amending actions in response to errors observed during unsuccessful training episodes, LearnAct systematically increases the efficiency and adaptability of actions undertaken by Large Language Model (LLM) agents.
The experiment conducted within the contexts of Robotic Planning and Alfworld environments demonstrated that LearnAct can significantly enhance agent performance on given tasks.

I hope this contributes to the great paper list!

[Lei Wang]

Hi, thank you for your contribution! We are currently preparing the third version of our survey, and we will certainly include this paper in our research and repository.