Reinforcement learning is direct adaptive optimal control

Neural network reinforcement learning methods are described and considered as a direct approach to adaptive optimal control of nonlinear systems. These methods have their roots in studies of animal learning and in early learning control work. An emerging deeper understanding of these methods is summarized that is obtained by viewing them as a synthesis of dynamic programming and stochastic approximation methods. The focus is on Q-learning systems, which maintain estimates of utilities for all state-action pairs and make use of these estimates to select actions. The use of hybrid direct/indirect methods is briefly discussed.>