Neural networks for control
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Reinforcement Learning
Neuro-Dynamic Programming
Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems
Mathematics of Operations Research
The optimizing-simulator: merging simulation and optimization using approximate dynamic programming
WSC '05 Proceedings of the 37th conference on Winter simulation
Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems
INFORMS Journal on Computing
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
An algorithm for approximating piecewise linear concave functions from sample gradients
Operations Research Letters
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There is a wide range of simulation problems that involve making decisions during the simulation, where we would like to make the best decisions possible, taking into account not only what we know when we make the decision, but also the impact of the decision on the future. Such problems can be formulated as dynamic programs, stochastic programs and optimal control problems, but these techniques rarely produce computationally tractable algorithms. We demonstrate how the framework of approximate dynamic programming can produce near-optimal (in some cases) or at least high quality solutions using techniques that are very familiar to the simulation community. The price of this challenge is that the simulation has to be run iteratively, using statistical learning techniques to produce the desired intelligence. The benefit is a reduced dependence on more traditional rule-based logic.