In the context of Reinforcement Learning, what does it mean to have a multi-dimensional continuous action space?
I came across the following in the COBRA Paper
A method for learning a distribution over a multi-dimensional continuous action space. This learned distribution can be sampled efficiently.
During the initial exploration phase it explores its environment, in which it can move objects freely with a continuous action space but is not rewarded for its actions.
So, what do the multi-dimensionality and the continuity of the action space refer to? It'd be great if someone could provide an explanation with examples!