Abstract:Many real-world problems require sequential decisions under uncertainty: when to inject or withdraw gas from storage, how to rebalance a pension portfolio each month, what temperature profile to run through a pharmaceutical reactor chain. Dynamic programming solves small instances exactly but scales exponentially in state dimensions. Black-box reinforcement learning handles high-dimensional states but trains slowly and produces no sensitivities.
We introduce SNAPO (Smooth Neural Adjoint Policy Optimization), a framework that embeds a neural policy inside a known, differentiable simulator, replaces hard constraints with smooth approximations, and computes exact gradients of the objective with respect to all policy parameters and all inputs in a single adjoint pass.
We demonstrate SNAPO on three domains: natural gas storage (training in under a minute, 365 forward curve sensitivities at no additional cost per sensitivity), pension fund asset-liability management (6.5x-200x sensitivity speedup over bump-and-revalue, scaling with the number of risk factors), and pharmaceutical manufacturing (cross-unit sensitivities through a 4-unit process chain, with 20 ICH Q8 regulatory sensitivities from 5 adjoint passes in 74.5 milliseconds).
All sensitivities are produced by the same backward pass that trains the policy, at a cost proportional to one reverse pass regardless of how many sensitivities are computed.
| Comments: | 27 pages, 8 tables. Three domains: natural gas storage, pension fund ALM, pharmaceutical manufacturing. Benchmark code and trained policies available on request |
| Subjects: | Machine Learning (cs.LG); Optimization and Control (math.OC); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF); Risk Management (q-fin.RM) |
| MSC classes: | 49J20, 65K10, 90C30, 93E20 |
| ACM classes: | G.1.6; I.2.6 |
| Cite as: | arXiv:2605.06570 [cs.LG] |
| (or arXiv:2605.06570v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06570 arXiv-issued DOI via DataCite (pending registration) |
From: Natalija Karpichina [view email]
[v1]
Thu, 7 May 2026 17:01:13 UTC (181 KB)
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