Stochastic Learning and Optimization
Publication year: 2007
ISBN: 978-0-387-69082-7
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(Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis (PA) of discrete-event dynamic systems, Markov decision processes (MDP)s), reinforcement learning (RL), and adaptive control, within a unified framework. (A simple approach to MDPs) This book introduces MDP theory through a simple approach based on performance difference formulas. This approach leads to results for the n-bias optimality with long-run average-cost criteria and Blackwell's optimality without discounting.
Subject: Computer Science, Computer, Markov Chains, Markov decision processes, Operations Research, calculus, ergodic systems, event based optimization, identification and adaptive control, optimization, perturbation analysis, programming, queueing systems, reinforcement learning, robot, stochastic approximation
