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Introduces a mixture genetic algorithm and reinforcement learning computation model used for independent agent learning in continuous, distributive, open environment, which takes full advantage of the reactive and robust of reinforcement learning algorithm and the property that genetic algorithm is suitable to the problem with high dimension,large collectivity, complex environment, and concludes that through proper training, the result verifies that this method is available in the complex multi agent environment.
Introduces a mixture genetic algorithm and reinforcement learning computation model used for independent agent learning in continuous, distributive, open environment, which takes full advantage of the reactive and robust of reinforcement learning algorithm and the property that genetic algorithm is suitable to the problem with high dimension , large collectivity, complex environment, and concludes that through proper training, the result verifies that this method is available in the complex multi agent environment.