Neural Approximations for Optimal Control and Decisionprovides a comprehensive methodology
for the approximate solution of functional optimization problems using neural networks and
other nonlinear approximators where the use of traditional optimal control tools is prohibited
by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state
and control vectors, etc. Features of the text include: • a general functional optimization
framework; • thorough illustration of recent theoretical insights into the approximate solutions
of complex functional optimization problems; • comparison of classical and neural-network
based methods of approximate solution; • bounds to the errors of approximate solutions; •
solution algorithms for optimal control and decision in deterministic or stochastic environments
with perfect or imperfect state measurements over a finite or infinite time horizon and with one
decision maker or several; • applications of current interest: routing in communications
networks, traffic control, water resource management, etc.;and • numerous, numerically
detailed examples.
