Error bound analysis of policy iteration based approximate dynamic programming for deterministic discrete-time nonlinear systems
作者： Wentao Guo , Feng Liu , Jennie Si , Shengwei Mei , Rui Li
DOI: 10.1109/IJCNN.2015.7280783
关键词:
摘要: Extensive approximate dynamic programming (ADP) algorithms have been developed based on policy iteration. For iteration ADP of deterministic discrete-time nonlinear systems, existing literature has proved its convergence in the formulation undiscounted value function under assumption exact approximation. Furthermore, error bound analyzed a discounted with consideration approximation errors. However, there not any analysis In this paper, we intend to fill theoretical gap. We provide sufficient condition error, so that iterative can be bounded neighbourhood optimal function. To best authors' knowledge, is first result for systems considering
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