  Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for
solving multi-stage stochastic optimization, widely used for modeling
real-world process optimization tasks. Unfortunately, SDDP has a worst-case
complexity that scales exponentially in the number of decision variables, which
severely limits applicability to only low dimensional problems. To overcome
this limitation, we extend SDDP by introducing a trainable neural model that
learns to map problem instances to a piece-wise linear value function within
intrinsic low-dimension space, which is architected specifically to interact
with a base SDDP solver, so that can accelerate optimization performance on new
instances. The proposed Neural Stochastic Dual Dynamic Programming ($\nu$-SDDP)
continually self-improves by solving successive problems. An empirical
investigation demonstrates that $\nu$-SDDP can significantly reduce problem
solving cost without sacrificing solution quality over competitors such as SDDP
and reinforcement learning algorithms, across a range of synthetic and
real-world process optimization problems.
