Three approaches to train echo state network actors of adaptive critic design

Abstract

The paper compares three approaches to train Echo state network (ESN) actors of Adaptive Critic Design (ACD): the classical gradient-based learning rule and two associative learning rules. First associative rule exploits the Hebbian learning law of the Adaptive Search Element from the seminal paper of Barto et al., while the the second one uses the Temporal Difference (TD) error for both critic and actor elements. The proposed learning approaches were applied to optimization of a complex nonlinear process for bio-polymer production. The comparison of the obtained results was done with respect to the convergence speed as well as to the reached local optima.