title: A simplification of the backpropagation-through-time algorithm for optimal neurocontrol.
abstract: Backpropagation-through-time (BPTT) is the temporal extension of backpropagation which allows a multilayer neural network to approximate an optimal state-feedback control law provided some prior knowledge (Jacobian matrices) of the process is available. In this paper, a simplified version of the BPTT algorithm is proposed which more closely respects the principle of optimality of dynamic programming. Besides being simpler, the new algorithm is less time-consuming and allows in some cases the discovery of better control laws. A formal justification of this simplification is attempted by mixing the Lagrangian calculus underlying BPTT with Bellman-Hamilton-Jacobi equations. The improvements due to this simplification are illustrated by two optimal control problems: the rendezvous and the bioreactor.
authors.affiliation: IRIDIA-CP, Univ. Libre de Bruxelles.
authors.forename: H
authors.initials: H
authors.lastname: Bersini
authors.forename: V
authors.initials: V
authors.lastname: Gorrini