Deep Learning Approximation for Stochastic Control Problems

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Summary

This work develops a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling and approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics.

Type
preprint
Published
2016-11-02
Cited by
225
References
21
Access
Open access

Keywords

Control (management), Mathematical economics, Computer science, Artificial intelligence, Economics

References

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