LOCALLY WEIGHTED LEARNING FOR CONTROL

Lazy learning methods provide useful representations and training algo rithms for learning about complex phenomena during autonomous adaptive control of complex systems, This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks, We explain various forms that control tasks can take, a nd how this affects the choice of learning paradigm. The discussion se ction explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.