I don't like the notion of using different decision theories depending on the situation, because the very idea of a decision theory is that it is consistent and comprehensive. Now if TDT were formulated as a plugin that seamlessly integrated into CDT in such a way that the resulting decision theory could be applied to any and all problems and would always yield optimal results, then that would be reason for me to learn about TDT. However, from what I gathered this doesn't seem to be the case?
TDT performs exactly as well as CDT on the class of problems CDT can deal with, because for those problems it essentially is CDT. So in practice you just use normal CDT algorithms except for when counterfactual copies of yourself are involved. Which is what TDT does.
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.