Neural approximators for the solution of decentralized optimal control problems
Marco Baglietto, Thomas Parisini, R. Zoppoli · 1999
There are many situations, in engineering and economic systems, where several decision makers (DMs), sharing different information patterns, cooperate to the accomplishment of a common goal. We address an approximate technique consisting in constraining the control functions to have a fixed structure (we chose feedforward neural networks). We are then able to obtain solutions that approximate the optimal ones within any desired degree of accuracy under very general conditions. Such a technique has proved to be effective in non-LQG classical optimal control and in team problems not solvable analytically.