Original Paper: https://arxiv.org/abs/2406.04784
By: Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang
Abstract:
Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents often face challenges in achieving high-level goals without detailed instructions and in adapting to environments where feedback is delayed. In this paper, we present SelfGoal, a novel automatic approach designed to enhance agents' capabilities to achieve high-level goals with limited human prior and environmental feedback. The core concept of SelfGoal involves adaptively breaking down a high-level goal into a tree structure of more practical subgoals during the interaction with environments while identifying the most useful subgoals and progressively updating this structure. Experimental results demonstrate that SelfGoal significantly enhances the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments. Project page:
In the ever-evolving landscape of artificial intelligence, autonomous language agents powered by large language models (LLMs) are gaining prominence as decision-making tools across diverse domains such as gaming, programming, and more. However, equipping these agents to consistently achieve high-level goals without frequent retraining remains a challenging task. Enter SELFGOAL, a novel framework designed to enable language agents to dynamically adapt and excel in achieving complex high-level goals. This blog post delves into the intricacies of SELFGOAL, highlighting its methodologies, key findings, and the implications of its application.
Imagine an AI agent tasked with the high-level goal of "winning the most money" in a competitive auction or "succeeding in a competition." These goals are broad and ambiguous, presenting significant challenges due to their inherent complexity and the delayed nature of rewards. Traditional approaches often fall short, either by decomposing tasks without grounding them in the environment or by summarizing experiences in a manner that's too general to be actionable. This is where SELFGOAL steps in, offering a self-adaptive framework that bridges these gaps.
At the heart of SELFGOAL lies the concept of adaptively breaking down a high-level goal into a hierarchical tree structure of practical subgoals, dynamically refined based on environmental feedback. The framework is built around three core modules: