论文标题
通过深入的增强学习的广泛计划
Generalized Planning With Deep Reinforcement Learning
论文作者
论文摘要
智力的标志是能够从例子中推断出一般原则的能力，这是正确的范围。广义计划涉及为一类规划问题寻找此类原则，因此可以使用域的小实例发现的原则来解决同一领域的更大实例。在这项工作中，我们研究了深入强化学习和图形神经网络的使用来学习这种广泛的政策，并证明它们可以概括为比受过训练的数量级大的实例。
A hallmark of intelligence is the ability to deduce general principles from examples, which are correct beyond the range of those observed. Generalized Planning deals with finding such principles for a class of planning problems, so that principles discovered using small instances of a domain can be used to solve much larger instances of the same domain. In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on.
