Despite the benefits mentioned in previous works of automatically acquiring skills for using them in hierarchical reinforcement learning algorithms such as solving the curse of dimensionality, improving exploration, and speeding up value propagation, they have not paid much attention to evaluating the effect of each skill on these factors. In this paper, we show that depending on the given task, a skill may be useful for learning it or not. In addition, the focus of the related work of automatically acquiring skills is on detecting subgoals, i.e., the skill termination condition, but there is not a precise method for extracting the initiation set of skills. In this paper, we propose not only two methods for evaluating skills but also two other methods for pruning the initiation set of them. Experimental results show significant improvements in learning different test domains after evaluating and pruning skills.

中文翻译：

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评估等级强化学习的技能

尽管在先前的工作中提到了自动获得技能以在层次强化学习算法中使用它们的好处，例如解决维数的诅咒，改善探索和加速价值传播，但他们并没有非常重视评估每种技能在以下方面的效果：这些因素。在本文中，我们表明，根据给定的任务，一项技能对于学习或不学习可能有用。另外，自动获取技能的相关工作的重点在于检测子目标（即技能终止条件），但是没有一种精确的方法可以提取技能的初始集合。在本文中，我们不仅提出了两种评估技能的方法，而且还提出了两种其他方法来修剪他们的技能。