Nelder-Mead simplex modifications for simulation optimization
Management Science
Neuro-Dynamic Programming
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
Technical Update: Least-Squares Temporal Difference Learning
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning tetris using the noisy cross-entropy method
Neural Computation
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Approximate Dynamic Programming for Ambulance Redeployment
INFORMS Journal on Computing
Ambulance redeployment: an approximate dynamic programming approach
Winter Simulation Conference
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Dynamic programming formulations may be used to solve for optimal policies in Markov decision processes. Due to computational complexity dynamic programs must often be solved approximately. We consider the case of a tunable approximation architecture used in lieu of computing true value functions. The standard methodology advocates tuning the approximation architecture via sample path information and regression to get a good fit to the true value function. We provide an example which shows that this approach may unnecessarily lead to poorly performing policies and suggest direct search methods to find better performing value function approximations. We illustrate this concept with an application from ambulance redeployment.