A new abstract artificial neural architecture for solving difficult learning control problems

摘要

We introduce a neural architecture designed for efficient, reliable, and adjustable solutions to difficult learning control problems. It consists of a hierarchy of command and control cneters governing motor selection networks. Within the controller, internal drives, similar to biological ones, facilitate learning. The inverted pendulum control problem is used to validate the proposed architecture.