What is meant by a multi-dimensional continuous action space? 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.

and

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!
Let me rephrase it a little - it's a multidimensional continuous space of actions. So, you assign each action some vector from $R^{n}$. For intuition -- imagine you have a robot arm with four joints. For every joint you could applied a rotation force from [-1, 1] and thus you get a 4-D vector with float numbers for each possible action.