Non-parametric approximate dynamic programming via the kernel method
This paper presents a novel and practical non-parametric approximate dynamic programming (ADP) algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees. In particular, we establish both theoretically and computationally that our proposal can serve as a viable replacement to state of the art parametric ADP algorithms, freeing the designer from carefully specifying an approximation architecture. We accomplish this by "kernelizing" a recent mathematical program for ADP (the "smoothed" approximate LP) proposed by Desai et al. (2011).