ReAcTree: Hierarchical Task Planning with Dynamic Tree Expansion using LLM Agent Nodes
Keywords: Task planning, large language model, decision-making, behavior tree, hierarchical planning
Abstract: Recent advancements in task planning using large language models (LLMs) have made remarkable progress. However, most existing methods, such as ReAct, face limitations when handling complex, long-horizon tasks due to inefficiencies in processing entire tasks through a single sequential decision-making process. To address these challenges, we propose ReAcTree, a hierarchical task planning method that automatically decomposes complex tasks into manageable subgoals within a tree structure. This tree consists of control flow nodes, which manage the execution order of agent nodes, and agent nodes that reason, act, and expand nodes into subgoals to achieve their goals. To further enhance performance, we introduce memory systems: each agent node retrieves goal-specific, agent-level experiences from episodic memory to use as in-context examples, and all agent nodes share and recall information obtained during task execution via working memory. Experiments on the WAH-NL dataset demonstrate that ReAcTree consistently outperforms ReAct across various LLMs and model sizes. For example, when using Qwen2.5 72B, ReAcTree achieves a goal success rate of 63\%, significantly surpassing ReAct's 24\%.
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 5394
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