  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.
