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• W2811111819 abstract "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." @default.
• W2811111819 created "2018-07-10" @default.
• W2811111819 creator A5046874748 @default.
• W2811111819 creator A5064113904 @default.
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• W2811111819 date "2018-06-28" @default.
• W2811111819 modified "2023-09-23" @default.
• W2811111819 title "Hierarchical Reinforcement Learning with Abductive Planning." @default.
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