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It combines reinforcement learning (RL) and approximate Bayesian computation sequential Monte Carlo (ABC-SMC) instead of ABC to improve the process of model selection and parameter estimation, where RL is used for model selection and ABC-SMC is exploited for parameter estimation. Numerical simulations illustrate that the combined algorithm (RL-ABC-SMC) significantly outperforms the ABC-SMC algorithm in terms of model selection. 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