An additive reinforcement learning

Abstract

In reinforcement learning, preparing basis functions requires a certain amount of prior knowledge and is in general a difficult task. To overcome this difficulty, an adaptive basis function construction technique has been proposed by Keller et al. recently, but it requires excessive computational cost. We propose an efficient approach to this context, in which the problem of approximating the value function is naturally decomposed into a number of sub-problems, each of which can be solved at small computational cost. Computer experiments show that the cpu-time needed by our method is much smaller than that of the existing method. © 2009 Springer Berlin Heidelberg.