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Approximate dynamic programming via iterated Bellman inequalities
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DOI：10.1002/rnc.3152.png)
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In this paper, we introduce new methods for finding functions that lower bound the value function of a stochastic control problem, using an iterated form of the Bellman inequality. Our method is based on solving linear or semidefinite programs, and produces both a bound on the optimal objective, as well as a suboptimal policy that appears to works very well. These results extend and improve bounds obtained in a previous paper using a single Bellman inequality condition. We describe the methods in a general setting and show how they can be applied in specific cases including the finite state case, constrained linear quadratic control, switched affine control, and multi-period portfolio investment. Copyright (c) 2014 John Wiley & Sons, Ltd.
Keyword:
convex optimization
dynamic programming
stochastic control
期刊
IF:
3.2
论文数:
7.0K
被引数:
1.4W
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