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Sampled differential dynamic programming (SaDDP) is a Monte Carlo variant of differential dynamic programming.<ref>{{Cite web|url=https://ieeexplore.ieee.org/document/7759229|title=Sampled differential dynamic programming - IEEE Conference Publication|website=ieeexplore.ieee.org|language=en-US|access-datedoi=2018-10-19.1109/IROS.2016.7759229}}</ref><ref>{{Cite web|url=https://ieeexplore.ieee.org/document/8430799|title=Regularizing Sampled Differential Dynamic Programming - IEEE Conference Publication|website=ieeexplore.ieee.org|language=en-US|access-date=2018-10-19}}</ref><ref>{{Cite journalbook|last=Joose|first=Rajamäki|date=2018|title=Random Search Algorithms for Optimal Control|url=http://urn.fi/URN:ISBN:978-952-60-8156-4|language=en|issn=1799-4942|isbn=9789526081564|publisher=Aalto University}}</ref> It is based on treating the quadratic cost of differential dynamic programming as the energy of a [[Boltzmann distribution]]. This way the quantities of DDP can be matched to the statistics of a [[Multivariate normal distribution|multidimensional normal distribution]]. The statistics can be recomputed from sampled trajectories without differentiation.
