  We investigate the challenge of task planning for multi-task embodied agents
in open-world environments. Two main difficulties are identified: 1) executing
plans in an open-world environment (e.g., Minecraft) necessitates accurate and
multi-step reasoning due to the long-term nature of tasks, and 2) as vanilla
planners do not consider how easy the current agent can achieve a given
sub-task when ordering parallel sub-goals within a complicated plan, the
resulting plan could be inefficient or even infeasible. To this end, we propose
"$\underline{D}$escribe, $\underline{E}$xplain, $\underline{P}$lan and
$\underline{S}$elect" ($\textbf{DEPS}$), an interactive planning approach based
on Large Language Models (LLMs). DEPS facilitates better error correction on
initial LLM-generated $\textit{plan}$ by integrating $\textit{description}$ of
the plan execution process and providing self-$\textit{explanation}$ of
feedback when encountering failures during the extended planning phases.
Furthermore, it includes a goal $\textit{selector}$, which is a trainable
module that ranks parallel candidate sub-goals based on the estimated steps of
completion, consequently refining the initial plan. Our experiments mark the
milestone of the first zero-shot multi-task agent that can robustly accomplish
70+ Minecraft tasks and nearly double the overall performances. Further testing
reveals our method's general effectiveness in popularly adopted non-open-ended
domains as well (i.e., ALFWorld and tabletop manipulation). 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.
