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[交流] [书籍]Simulation-based Algorithms for Markov Decision Processes[已搜无重复]

Simulation-based Algorithms for Markov Decision Processes
Springer | ISBN 1846286891 | 2007-03-05 | PDF | 207 pages | 4.6 MB

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. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include:

• multi-stage adaptive sampling;

• evolutionary policy iteration;

• evolutionary random policy search; and

• model reference adaptive search.

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