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Race through the different cours Neuro-dynamic programming is the same as dynamic programming except that the former has the concept of approximation architectures.
It combines artificial intelligence , simulation-base algorithms, and functional approach techniques.
It means learning how to make improved decisions for the future via built-in mechanism based on the current behavior. The most important part of neuro-dynamic programming is to build a trained neuro network for the optimal problem.
Simulation based optimization has some limitations, such as the difficulty of creating a model that imitates the dynamic behavior of a system in a way that is considered good enough for its representation.
Another problem is complexity in the determining uncontrollable parameters of both real-world system and simulation.
Moreover, only a statistical estimation of real values can be obtained. It is not easy to determine the objective function, since it is a result of measurements, which can be harmful for the solutions.
From Wikipedia, the free encyclopedia. IEEE Computer Society, Handbook of Simulation Optimization. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control.
Hoboken: Wiley. Artificial Intelligence in Medicine. Approximate Dynamic Programming Solving the Curses of Dimensionality 2nd ed. Introduction to Derivative-Free Optimization.
MPS-SIAM Book Series on Optimization. Philadelphia: SIAM. Retrieved Optimization of discrete event systems via simultaneous perturbation stochastic approximation.
IIE Transactions 29, — Introduction to dynamic programming. Neuro-dynamic programming approach to retailer inventory management. Proceedings of the IEEE Conference on Decision and Control, 4 , Simulation-based optimization for complex stochastic systems.
University of Washington. Simulation-based Optimization, ProQuest Dissertations and Theses. Categories : Mathematical optimization Simulation.
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