One of the key challenges in applying reinforcement learning to real-life
problems is that the amount of train-and-error required to learn a good policy
increases drastically as the task becomes complex. One potential solution to
this problem is to combine reinforcement learning with automated symbol
planning and utilize prior knowledge on the domain. However, existing methods
have limitations in their applicability and expressiveness. In this paper we
propose a hierarchical reinforcement learning method based on abductive
symbolic planning. The planner can deal with user-defined evaluation functions
and is not based on the Herbrand theorem. Therefore it can utilize prior
knowledge of the rewards and can work in a domain where the state space is
unknown. We demonstrate empirically that our architecture significantly
improves learning efficiency with respect to the amount of training examples on
the evaluation domain, in which the state space is unknown and there exist
multiple goals.

本文提出了一种基于阿布达比符号规划的层次强化学习方法，该规划器可以处理用户定义的评估函数，并且不基于 Herbrand 定理。因此，它可以利用奖励的先前知识，并且可以在状态空间未知的领域中工作。我们在实验中证明了我们的体系架构在未知状态空间和多目标存在时，相对于评估领域的训练样例数量，可以显著提高学习效率。