quotation:[Copy]
Warren B. POWELL,Jun MA.[en_title][J].Control Theory and Technology,2011,9(3):336~352.[Copy]
【Print page】 【Online reading】【Download 【PDF Full text】 View/Add CommentDownload reader Close

←Previous page|Page Next →

Back Issue    Advanced search

This Paper:Browse 3265   Download 364 本文二维码信息
码上扫一扫!
WarrenB.POWELL,JunMA
0
(Department of Operations Research and Financial Engineering, Princeton University)
摘要:
关键词:  
DOI:
Received:December 23, 2010Revised:December 23, 2010
基金项目:
A review of stochastic algorithms with continuous value function approximation and some new approximate policy iteration algorithms for multidimensional continuous applications
Warren B. POWELL,Jun MA
(Department of Operations Research and Financial Engineering, Princeton University)
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.
Key words:  Approximate dynamic programming  Reinforcement learning  Optimal control  Approximation algorithms