A Machine Learning Approach to Policy Optimization in System Dynamics Models

The paper proposes a policy design method for system dynamics models based on recurrent neural networks. A policy maker first directly creates an arbitrary desired reference mode and run the algorithm to search for the most appropriate model(s) automatically to fit it. In the searching process, both the system structure and its parameter values evolve simultaneously. Several experiments are conducted to evaluate our approach. The results show that our approach is as good as or better than other comparable methods. Copyright © 2011 John Wiley & Sons, Ltd.

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