Computing Near Optimal Strategies for Stochastic Investment Planning Problems
نویسندگان
چکیده
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 MonteCarlo estimation techniques to obtain novel and efficient algorithms that closely approximate the optimal strategies.
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