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Parallel Recombinative Reinforcement Learning: A Genetic Approach
1996
Journal of Intelligent Systems
A technique is presented that is suitable for function optimization in high-dimensional binary domains. The method allows an efficient parallel implementation and is based on the combination of genetic algorithms and reinforcement learning schemes. More specifically, a population of probability vectors is considered, each member corresponding to a reinforcement learning optimizer. Each probability vector represents the adaptable parameters of a team of stochastic units whose binary outputs
doi:10.1515/jisys.1996.6.2.145
fatcat:wdifpbejjjco7dgmyeiwhaae64