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• W2044287460 abstract "We review the literature on approximate dynamic programming, with the goal of better understanding the theory behind practical algorithms for solving dynamic programs with continuous and vector-valued states and actions and complex information processes. We build on the literature that has addressed the well-known problem of multidimensional (and possibly continuous) states, and the extensive literature on model-free dynamic programming, which also assumes that the expectation in Bellman’s equation cannot be computed. However, we point out complications that arise when the actions/controls are vector-valued and possibly continuous. We then describe some recent research by the authors on approximate policy iteration algorithms that offer convergence guarantees (with technical assumptions) for both parametric and nonparametric architectures for the value function." @default.
• W2044287460 created "2016-06-24" @default.
• W2044287460 creator A5049064082 @default.
• W2044287460 creator A5054833334 @default.
• W2044287460 date "2011-07-19" @default.
• W2044287460 modified "2023-10-17" @default.
• W2044287460 title "A review of stochastic algorithms with continuous value function approximation and some new approximate policy iteration algorithms for multidimensional continuous applications" @default.
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• W2044287460 doi "https://doi.org/10.1007/s11768-011-0313-y" @default.
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