Genetic Algorithms for Approximating Solutions to POMDPs
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چکیده
We use genetic algorithms (GAs) to nd good nite horizon policies for POMDPs, where the search is limited to policies with a xed nite amount of policy memory. Initial results were presented in (Lusena et al. 1999) with one GA. In this paper, diierent cross-over and mutation rates are compared. Initializing the population of the genetic algorithm is done using smaller genetic algorithms. The selection and termination criteria are altered and tested. Reordering and uniform cross-over are implemented and compared against the original GA. And nally, a steady-state GA is implemented and used as a control for tests with a steady-state GA whose operator rates are dynamic.
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تاریخ انتشار 1999