There is a growing interest in the application of Reinforcement Learning (RL) techniques to AI planning with the aim to come up with general policies. Typically, the mapping of the transition model of AI planning to the state transition system of a Markov Decision Process is established by assuming a one-to-one correspondence of the respective action spaces. In this paper, we introduce the concept of meta-operator as the result of simultaneously applying multiple planning operators, and we show that including meta-operators in the RL action space enables new planning perspectives to be addressed using RL, such as parallel planning. Our research aims to analyze the performance and complexity of including meta-operators in the RL process, concretely in domains where satisfactory outcomes have not been previously achieved using usual generalized planning models. The main objective of this article is thus to pave the way towards a redefinition of the RL action space in a manner that is more closely aligned with the planning perspective.

通过引入元操作符的概念，将元操作符包括在强化学习中的行动空间中，可以通过强化学习实现新的规划视角，如并行规划。本研究的主要目标是分析在强化学习过程中包含元操作符的性能和复杂性，具体应用于以往常规广义规划模型无法实现满意结果的领域，从而为重新定义与规划视角更加密切相关的强化学习行动空间铺平道路。

使用深度强化学习实现并行规划的元算符