General-purpose RLADP: Solving the Scaling Problem

Abstract: Years ago, RLADP already reported breakthrough capabilities of great practical importance in applications such as carbon-carbon parts production (crucial to the Dreamliner), missile interception, aircraft control, low-pollution engine control and turbogenerator control, demonstrating effective control of highly nonlinear plants with 5-20 state variables. But the further advance of RLADP has been held back by the problem of 'value function approximation" for systems like electric power grids, large-scale logistics systems and vertebrate brains, where one must cope with thousands or millions of variables.
Over the past two years, "deep learning" groups have defied conventional wisdom by actually implementing advanced neural network designs to handle such complexity, and thereby broken world records for image processing, speech recognition, natural language processing and other widely competed benchmarks. This talk discusses how these and other neural network concepts can solve the scaling problem in RLADP. Connections to operations research and some new stability work will also be discussed as time permits.