Abstract: A network optimization method, a device, and a non-transitory computer-readable storage medium are disclosed. The method may include: modeling problems existing in cells in a first region to obtain N agents, a modeling method and a training method, where a proportion of cells in which existing problems belong to a same problem category among the cells contained in the first region is greater than or equal to a preset threshold, geographic locations of the cells contained are consecutive, and an outline of the first region is an outwardly convex figure, and N is an integer greater than or equal to 1; and for each of the agents, determining an initial model of the agent according to the modeling method and the training method, and training the initial model of the agent using a reinforcement learning method according to the modeling method and the training method.

Abstract: A network optimization method, a device, and a non-transitory computer-readable storage medium are disclosed. The method may include: modeling problems existing in cells in a first region to obtain N agents, a modeling method and a training method, where a proportion of cells in which existing problems belong to a same problem category among the cells contained in the first region is greater than or equal to a preset threshold, geographic locations of the cells contained are consecutive, and an outline of the first region is an outwardly convex figure, and N is an integer greater than or equal to 1; and for each of the agents, determining an initial model of the agent according to the modeling method and the training method, and training the initial model of the agent using a reinforcement learning method according to the modeling method and the training method.