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Implicit Learning of Compiled Macro-Actions for Planning

机译:隐含学习编译的宏动作进行规划

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We build a comprehensive macro-learning system and contribute in three different dimensions that have previously not been addressed adequately. Firstly, we learn macro-sets considering implicitly the interactions between constituent macros. Secondly, we effectively learn macros that are not found in given example plans. Lastly, we improve or reduce degradation of plan-length when macros are used; note, our main objective is to achieve fast planning. Our macro-learning system significantly outperforms a very recent macro-learning method both in solution speed and plan length.
机译:我们建立了一个全面的宏观学习系统,并在以前没有充分解决的三个不同维度。首先,我们学习宏观集,考虑隐式地考虑组成宏之间的交互。其次,我们有效学习在给定示例计划中找不到的宏。最后,当使用宏时,我们改善或减少计划长度的降低;注意,我们的主要目标是实现快速规划。我们的宏观学习系统在解决方案速度和计划长度中显着优于最近的宏观学习方法。

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