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. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, compute...
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...
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer s...
The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing, a population-based on-line simulation-based algorithm; game-theoretic method for solving MDPs and more.
The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing,...
