Computing near optimal strategies for stochastic investment planning problems

Authors:
Milos Hauskrecht;Gopal Pandurangan;Eli Upfal
Affiliations:
Computer Science Department, Brown University, Providence, RI;Computer Science Department, Brown University, Providence, RI;Computer Science Department, Brown University, Providence, RI
Venue:
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Year:
1999

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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.