Open sbenthall opened 2 years ago
See #1186 -- work on 'frames' has shifted over to 'stages', which have a better design more focused on Bellman equations.
This can be in HARK.algos
and have a similar form after this PR is merged:
https://github.com/econ-ark/HARK/pull/1283
I did a preliminary look into this. I think the key issue might be that the post-value function $v_y$ and the resulting value function $v$ both need to be auto-differentiable for this to be chained from period to period (or stage to stage, or whatever). I believe it's possible for the action-value function (e.g. $u(c) + \beta v_y(m-c)$) to be used as a loss function directly.
@alanlujan91 Aha yes the Deep Equilbrium Nets approach seems quite promising :)
A nice-to-have feature based on the conversation today ...
A general solver to models defined using
HARK.frame
that uses a (deep) neural network for its solution/policy functions.The idea is to use Tensorflow to simulation the model forward and use the resulting utility as a loss function, which is then reduced through deep learning training techniques.
Some work by Pablo for inspiration:
https://notes.quantecon.org/submission/5ddb3c926bad3800109084bf