There's a Haskell implementation of modal UDT.

Previous discussion of modal UDT: 1, 2.

FDT is a family of decision theories, modal UDT is a specific algorithm. As stated it requires a hypercomputer, however has bounded variants.

The main obstacle is that these problems are almost always grossly underspecified. FDT depends a lot more upon the structure of a scenario than simpler decision theories.

A lesser but still valid obstacle is that there is no specification language for describing all the interrelations between the components of the scenario.

A third obstacle is that while FDT is relatively simple to define in mathematical theory, in practice it can result in enormous state spaces to search even when there is only one binary action to decide.

FDT operates on a causal graph. (Maybe the other requirements are also hard to satisfy?* I think this is the major obstacle.) You would probably have to create the graph, for the problem, then pass it to the program (if you write one).

One could argue that the trick in real world situations is figuring out the causal graph anyway.

This is rather disconcerting.

When people talk about Bayesian methods here, they don't seem like they're using code for the Monte-Carlo stuff, or stuff like that.

Edit:

*JBlack's answer indicates this the other requirements, are an issue.