Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the problem instance is especially challenging. A recent line of work focuses on learning planning parameter generators, but lack a consistent problem definition and software framework. This work proposes the unified planner optimization problem (POP) formulation, along with the Open Planner Optimization Framework (OPOF), a highly extensible software framework to specify and to solve these problems in a reusable manner.

翻译：确定内部规划参数对于最大限度地提高规划员的绩效至关重要,然而,以问题实例为条件自动调整内部参数尤其具有挑战性。最近的一项工作侧重于学习规划参数生成器,但缺乏一致的问题定义和软件框架。这项工作提出了统一的规划员优化问题(POPP)的拟订,同时提出了《开放规划员优化框架》(OPOF),这是一个非常可扩展的软件框架,可以用可重复使用的方式具体说明和解决这些问题。</s>