In recent years, with the continuous increase of deep learning models, especially deep reinforcement learning models, the training cost, that is, the search space of hyperparameters, has also continuously increased. However, most traditional hyperparameter search algorithms are based on sequential execution of training, which often takes weeks or even months to find a better hyperparameter configuration. In order to solve the problem of the long search time hyperparameters and the difficulty in finding a better hyperparameter of deep reinforcement learning configuration, this paper proposes a new hyper-parameter search algorithm, named asynchronous parallel hyperparameter search with population evolution. This algorithm combines the idea of evolutionary algorithms and uses a fixed resource budget to search the population model and its hyperparameters asynchronously and in parallel, thereby improving the performance of the algorithm. It is realized that a parameter search algorithm can run on the Ray parallel distributed framework. Experiments show that the parametric asynchronous parallel search based on population evolution on the parallel framework is better than the traditional hyperparameter search algorithm, and its performance is stable.