A CUDA-based parallel adaptive dynamic programming algorithm

Adaptive Dynamic Programming (ADP) with critic-actor architecture is a useful way to achieve online learning control. The algorithm Gaussian-Kernel Adaptive Dynamic Programming (GK-ADP) that has been developed before has a kind of two-phase iteration, which not only approximates value function, but also optimizes hyper-parameters simultaneously. However, just like most iteration algorithms are applied in practice, the scale of sample set will increase as the complexity of the system increases, and it will induce a high computation cost. In order to speed up computation, a practical acceleration method using parallel computation for GK-ADP is presented in this paper. To realize parallel computation, a high efficient configuration based on CUDA is designed, in which a group of GPUs work in parallel to compute the most complex part of GK-ADP. The comparison test illustrates that the computation burden which hinders GK-ADP's application is reduced to a large extent when the parallel computing is introduced.