Many 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 well as other 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 outperform 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.
Search
Reviews
[Write a review of this article]
Find related articles from these CiteULike users
