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Dynamic programming (DP) is an approach to computing the optimal control policy over time under nonlinearity and uncertainty by employing the principle of optimality introduced by Richard Bellman. Instead of enumerating all possible control sequences, dynamic programming only searches admissible state and/or action values that satisfy the principle of optimality. Therefore, the computation complexity...
Approximate dynamic programming (ADP) has been widely studied from several important perspectives: algorithm development, learning efficiency measured by success or failure statistics, convergence rate, and learning error bounds. Given that many learning benchmarks used in ADP or reinforcement learning studies are control problems, it is important and necessary to examine the learning controllers...
This paper shows an approach to integrating common approximate dynamic programming (ADP) algorithms into a theoretical framework to address both analytical characteristics and algorithmic features. Several important insights are gained from this analysis, including new approaches to the creation of algorithms. Built on this paradigm, ADP learning algorithms are further developed to address a broader...
This paper shows an approach to integrating common approximate dynamic programming (ADP) algorithms into a theoretical framework to address both analytical characteristics and algorithmic features. Several important insights are gained from this analysis, including new approaches to the creation of algorithms. Built on this paradigm, ADP learning algorithms are further developed to address a broader...
Nowadays, Web services are growing very fast and are usually aggregated into a composite one to satisfy customer's more and more complex requirements. Generally, there may be several different candidate services to carry out one task in a composite service, so a choice needs to be made for helping users select the most suitable one to satisfy their end-to-end constraints. This paper addresses this...
A neural network-based approximate dynamic programming (ADP) method, the direct neural dynamic programming (direct NDP), is introduced in this paper. The paper covers the basic principle of this learning scheme and an illustrative example of how direct NDP can be implemented. The paper focuses on how direct NDP can be applied to power system stability control. In this case direct NDP is based on realtime...
Direct NDP is in the family of approximate dynamic programming designs aiming at using learning and approximation methods to solve dynamic optimization problems formulated in dynamic programming, and to overcome the curse of dimensionality. Due to the statistical learning nature of the approaches, researchers usually make use of statistical measures to evaluate the design performance of the learning...
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