Abstract
In the paper, a potential-based policy iteration method is proposed for optimal control of a stochastic dynamic system with an average cost criterion and a parameterized control law. In this method, the potential function and the optimal control parameters are obtained via a least-squares-based approach. The potential estimation algorithm is derived from a temporal difference learning method, which can be viewed as a continuous version of the least-squares policy evaluation algorithm. The policy iteration algorithm is validated by solving a linear quadratic gaussian problem in the simulation.
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