论文标题
在使用图神经网络的大型问题实例中，在大量问题实例中进行了学习的计划
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
论文作者
论文摘要
现实世界中的计划问题通常涉及数百甚至数千个物体，扭曲了现代计划者的限制。在这项工作中，我们通过学习预测一小部分对象来应对这一挑战，这些对象足以找到计划。我们提出了一个图形神经网络体系结构，以预测单个推理通行证中的对象重要性，从而产生了很少的开销，同时大大减少了计划者必须考虑的对象数量。我们的方法将计划者和过渡模型视为黑匣子，并且可以与任何现成的计划者一起使用。从经验上讲，在经典的计划，概率计划以及机器人任务和运动计划中，我们发现我们的方法导致计划的速度明显快于几个基线，包括其他部分基础策略和提起的计划者。我们得出的结论是，学习预测一组计划问题的对象集是一种简单，有力且一般的规划机制，可在大规模实例中进行计划。视频：https：//youtu.be/fwsvjc2fvce代码：https：//git.io/jisqx
Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while greatly reducing the number of objects that must be considered by the planner. Our approach treats the planner and transition model as black boxes, and can be used with any off-the-shelf planner. Empirically, across classical planning, probabilistic planning, and robotic task and motion planning, we find that our method results in planning that is significantly faster than several baselines, including other partial grounding strategies and lifted planners. We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances. Video: https://youtu.be/FWsVJc2fvCE Code: https://git.io/JIsqX
