Information Relaxation Bounds for Infinite Horizon Markov Decision Processes

@article{Brown2017InformationRB,
  title={Information Relaxation Bounds for Infinite Horizon Markov Decision Processes},
  author={David B. Brown and Martin B. Haugh},
  journal={Operations Research},
  year={2017},
  volume={65},
  pages={1355-1379}
}
Copyright: © 2017 INFORMS Abstract. We consider the information relaxation approach for calculating performance bounds for stochastic dynamic programs (DPs), following Brown et al. [Brown DB, Smith JE, Sun P (2010) Information relaxations and duality in stochastic dynamic programs. Oper. Res. 58(4, Part 1):785–801]. This approach generates performance bounds by solving problems with relaxed nonanticipativity constraints and a penalty that punishes violations of these constraints. In this paper… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 37 references

Optimal policy for a multi - product , dynamic , nonstationary inventory system

  • A. F. Veinott
  • Management Science
  • 1965
Highly Influential
4 Excerpts

Monte Carlo Methods in Financial Engineering (Springer, New York)

  • P Glasserman
  • 2004
Highly Influential
3 Excerpts

Real and Complex Analysis (McGraw-Hill, New York)

  • W Rudin
  • Van Mieghem JA
  • 1987
Highly Influential
1 Excerpt

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