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An Approximate Stochastic Annealing algorithm for finite horizon Markov decision processes

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2 Author(s)
Jiaqiao Hu ; Dept. of Appl. Math. & Stat., State Univ. of New York, Stony Brook, NY, USA ; Hyeong Soo Chang

We present a simulation-based algorithm called Approximate Stochastic Annealing (ASA) for solving finite-horizon Markov decision processes (MDPs). The algorithm iteratively estimates the optimal policy by sampling from a sequence of probability distribution functions over the policy space. By exploiting a novel connection of ASA to the stochastic approximation method, we show that the sequence of distribution functions generated by the algorithm converges to a degenerated distribution that concentrates only on the optimal policy. Numerical examples are also provided to illustrate the algorithm.

Published in:

Decision and Control (CDC), 2010 49th IEEE Conference on

Date of Conference:

15-17 Dec. 2010