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Stochastic Learning and Optimization
A Sensitivity-based Approach
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Performance optimization is very important in designing and operating modern engineering systems in many areas, including communications (Internet and wireless), manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the systems parameters cannot be easily identified. Therefore, learning techniques have to be applied. Learning and optimization of stochastic systems is a multi-disciplinary area which has been attracting wide attention from researchers in many disciplines including control systems, operations research, and computer science. Areas such as perturbation analysis (PA) in discrete event dynamic systems (DEDSs), Markov decision processes (MDPs) in operations research, reinforcement learning (RL) or neuro-dynamic programming (NDP) in computer science, identification and adaptive control (I&AC) in control systems, share the common goal: to make the "best decision" to optimize the system performance. Different areas take different perspectives and have different formulations for the same goal. This book provides an overview for the above areas with a unified framework based on a sensitivity point of view.
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