Here, this state-of-the-art research is brought together in a way that makes it accessible to researchers of varying interests and backgrounds. Many specific algorithms, illustrative numerical examples and rigorous theoretical convergence results are provided. The algorithms differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning. The algorithms can be combined with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality.
Here, this state-of-the-art research is brought together in a way that makes it accessible to researchers of varying interests and backgrounds. Many specific algorithms, illustrative numerical examples and rigorous theoretical convergence results are provided. The algorithms differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning. The algorithms can be combined with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality.

Simulation-based Algorithms for Markov Decision Processes
189
Simulation-based Algorithms for Markov Decision Processes
189Paperback(Softcover reprint of hardcover 1st ed. 2007)
Product Details
ISBN-13: | 9781849966436 |
---|---|
Publisher: | Springer London |
Publication date: | 12/08/2010 |
Series: | Communications and Control Engineering |
Edition description: | Softcover reprint of hardcover 1st ed. 2007 |
Pages: | 189 |
Product dimensions: | 6.10(w) x 9.20(h) x 0.60(d) |