## AbstractMany stochastic dynamic programming tasks in continuous action-spaces are tackled through discretization. We here avoid discretization; then, approximate dynamic programming (ADP) involves (i) many learning tasks, performed here by Support Vector Machines, for Bellman-function-regression (ii) many non-linear- optimization tasks for action-selection, for which we compare many algorithms. We include discretizations of the domain as particular non-linear-programming-tools in our experiments, so that by the way we compare optimization approaches and discretization methods. We conclude that robustness is strongly required in the non-linear-optimizations in ADP, and experimental results show that (i) discretization is sometimes inefﬁcient, but some speciﬁc discretization is very efﬁcient for ”bang-bang” problems (ii) simple evolutionary tools out- perform quasi-random in a stable manner (iii) gradient-based techniques are much less stable (iv) for most high-dimensional ”less unsmooth” problems Covariance-Matrix-Adaptation is ﬁrst ranked.
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