In this paper, we study the problem of planning in Minecraft, a popular, democratized yet challenging open-ended environment for developing multi-task embodied agents. We've found two primary challenges of empowering such agents with planning: 1) planning in an open-ended world like Minecraft requires precise and multi-step reasoning due to the long-term nature of the tasks, and 2) as vanilla planners do not consider the proximity to the current agent when ordering parallel sub-goals within a complicated plan, the resulting plan could be inefficient. To this end, we propose "Describe, Explain, Plan and Select" (DEPS), an interactive planning approach based on Large Language Models (LLMs). Our approach helps with better error correction from the feedback during the long-haul planning, while also bringing the sense of proximity via goal Selector, a learnable module that ranks parallel sub-goals based on the estimated steps of completion and improves the original plan accordingly. Our experiments mark the milestone of the first multi-task agent that can robustly accomplish 70+ Minecraft tasks and nearly doubles the overall performances. Finally, the ablation and exploratory studies detail how our design beats the counterparts and provide a promising update on the $\texttt{ObtainDiamond}$ grand challenge with our approach. The code is released at https://github.com/CraftJarvis/MC-Planner.

翻译：在本文中,我们研究了在Minecraft中进行规划的问题,这是一个流行的、民主化的、但具有挑战性的开放型环境,用于发展多任务化剂。我们发现了在规划方面赋予此类剂以权力的两大主要挑战:(1) 在像Minecraft这样的开放世界中进行规划需要精确和多步推理,因为任务的长期性质使得任务具有长期性;(2) 香草规划者在一个复杂的计划中订购平行的次级目标时不考虑接近当前剂的问题,由此产生的计划可能是效率低下的。为此,我们建议采用基于大语言模型的互动规划方法(DEPS)“设计、解释、计划和选择 ” (DEPS ) 。我们的方法有助于从长期规划期间的反馈中更好地纠正错误,同时通过目标选择带来接近感,一个可学习模块,根据估计的完成步骤排列平行的次级目标,并相应改进原计划。我们实验标志着第一个能够强有力地完成70+ Minetracraft任务的多任务/ Special) 的里程碑。最后,Amblution and lovely stretitual streal a extitution ex