Computing Near Optimal Strategies for Stochastic Investment Planning Problems
Miloš Hauskrecht, Gopal Pandurangan, Eli Upfal · 1999
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