Stochastic learning and optimization : a sensitivity based approach ; with 27 tables and 212 problems Introduction Perturbation analysis Learning and optimization with perturbation analysis Markov decision processes Sample path based policy iteration Reinforcement learning Adaptive control problems as MDPs Event based optimization of Markov systems Constructing sensitivity formulas Probablility and Markov processes Stochastic matrices Queueing theory