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To accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. However function approximation reduces the accuracy of state value, and brings difficulty in the convergence. To solve the problems of tradeoff between the generalization and accuracy in reinforcement learning, we represent state-action value by two CMAC networks with different generalization parameters. The accuracy CMAC network can represent values exactly, whichdoi:10.1109/icpr.2006.416 dblp:conf/icpr/ZhengLL06 fatcat:dao3kuztxzbcdfvc2a3swlnuze