There's interesting research out of Deepmind on two parts of this, which are using the Transformer model in Reinforcement Learning contexts[0] and creating textual GANs[1].
As you are probably aware, GANs are one of the important tools that have driven forward image synthesis and until recently it was impossible to apply them to text, so I expect this to push us quite a bit forward. There's also ongoing work in the selection of the metric to use to evaluate the generated text, and discriminate between human and machine-generated text.
The ability to set your own goals and task yourself to achieve them is the essence of AI. Not "AI" as we know it today, but Sci-fi AI where it's a machine person.
Indeed, but that kind of describes the gulf between current language processing and present AI. Present AI generates tokens that seem to have meaning or seem like an appropriate response to a statement but where it become evident after 2-3 paragraphs, there's no substantial relation to either underlying meaning or underlying goals.
Part of this is "underlying meaning" is an intuitive way to describe things but whatever is underlying here is more tenuous than a classical logic/GOFAI model of the world but more "solid" than a long, clever stream of associations.
Is it? It's pretty clear that this kind of activity isn't that common in humans - mostly we pick up our goals from cues and drivers in our environment and society.
[0] https://arxiv.org/abs/1910.06764
[1] https://arxiv.org/abs/1905.09922