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Simulation-Based Algorithms for Markov Decision Processes (Hardcover, 2nd ed. 2013)
Loot Price: R2,946
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Simulation-Based Algorithms for Markov Decision Processes (Hardcover, 2nd ed. 2013)
Series: Communications and Control Engineering
Expected to ship within 10 - 15 working days
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Markov decision process (MDP) models are widely used for modeling
sequential decision-making problems that arise in engineering,
economics, computer science, and the social sciences. Many
real-world problems modeled by MDPs have huge state and/or action
spaces, giving an opening to the curse of dimensionality and so
making practical solution of the resulting models intractable. In
other cases, the system of interest is too complex to allow
explicit specification of some of the MDP model parameters, but
simulation samples are readily available (e.g., for random
transitions and costs). For these settings, various sampling and
population-based algorithms have been developed to overcome the
difficulties of computing an optimal solution in terms of a policy
and/or value function. Specific approaches include adaptive
sampling, evolutionary policy iteration, evolutionary random policy
search, and model reference adaptive search.
This substantially enlarged new edition reflects the latest
developments in novel algorithms and their underpinning theories,
and presents an updated account of the topics that have emerged
since the publication of the first edition. Includes:
innovative material on MDPs, both in constrained settings and with
uncertain transition properties;
game-theoretic method for solving MDPs;
theories for developing roll-out based algorithms; and
details of approximation stochastic annealing, a population-based
on-line simulation-based algorithm.
The self-contained approach of this book will appeal not only to
researchers in MDPs, stochastic modeling, and control, and
simulation but will be a valuable source of tuition and reference
for students of control and operations research.
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