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Reinforcement Learning and Adaptive Control

Adaptive control [19, 112] is also concerned with algorithms for improving a sequence of decisions from experience. Adaptive control is a much more mature discipline that concerns itself with dynamic systems in which states and actions are vectors and system dynamics are smooth: linear or locally linearizable around a desired trajectory. A very common formulation of cost functions in adaptive control are quadratic penalties on deviation from desired state and action vectors. Most importantly, although the dynamic model of the system is not known in advance, and must be estimated from data, the structure of the dynamic model is fixed, leaving model estimation as a parameter estimation problem. These assumptions permit deep, elegant and powerful mathematical analysis, which in turn lead to robust, practical, and widely deployed adaptive control algorithms.

Leslie Pack Kaelbling
Wed May 1 13:19:13 EDT 1996