Sparse grids and high dimension DP

I have two points.

First, given the growing popularity of models where distributions make the state, Python resources for (adaptive) sparse grids:

would very be useful. At the moment, we have There is also TASMANIAN (not from Tasmania):

Second, what are people’s thoughts on the various potential methods to do high dimension approximate dynamic programming? Adaptive sparse grids seem ideal, but require a bit of engineering. Another option seems to be neural nets (Bertsekas, Neureudynamic Programming), but I am not sure how one reduces the set of grid points. Both seem to suffer from convergence issues.

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