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Computing near Optimal Strategies for Stochastic Investment Planning Problems

Milos Hauskrecht, Gopal Pandurangan, Eli Upfal
IJCAI 1999 pp. 1310-1315
/ijcai/1999/hauskrecht1999ijcai-computing/

Abstract

We present efficient techniques for computing near optimal strategies for a class of stochastic commodity trading problems modeled as Markov decision processes (MDPs). The process has a continuous state space and a large action space and cannot be solved efficiently by standard dynamic programming methods. We exploit structural properties of the process, and combine it with Monte-Carlo estimation techniques to obtain novel and efficient algorithms that closely approximate the optimal strategies. 1

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Text

Hauskrecht et al. "Computing near Optimal Strategies for Stochastic Investment Planning Problems." International Joint Conference on Artificial Intelligence, 1999.

Markdown

[Hauskrecht et al. "Computing near Optimal Strategies for Stochastic Investment Planning Problems." International Joint Conference on Artificial Intelligence, 1999.](https://mlanthology.org/ijcai/1999/hauskrecht1999ijcai-computing/)

BibTeX

@inproceedings{hauskrecht1999ijcai-computing,
  title     = {{Computing near Optimal Strategies for Stochastic Investment Planning Problems}},
  author    = {Hauskrecht, Milos and Pandurangan, Gopal and Upfal, Eli},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1999},
  pages     = {1310-1315},
  url       = {https://mlanthology.org/ijcai/1999/hauskrecht1999ijcai-computing/}
}