A network optimization method performed by a network optimization device, comprising: modeling (100, 600, 700) problems existing in cells in a first region to obtain N agents, a modeling method and a training method, wherein 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 in the first region 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; or performing network optimization on a cell corresponding to the agent according to the trained initial model of the agent; or determining an initial model of the agent according to the modeling method and the training method, training the initial model of the agent using a reinforcement learning method according to the modeling method and the training method, and performing network optimization on a cell corresponding to the agent according to the trained initial model of the agent; characterized in that, the determination of the first region comprises: acquiring history performance data of all cells in a network; determining, for each of the cells, whether the cell is a normal cell or a problematic cell according to history performance data of the cell; dividing, into a same second region of at least one second region, cells in which existing problems belong to a same problem category among problematic cells; and determining, for each of the at least one second region, the first region according to a geographic location relationship of cells contained in the respective second region and the outline of the respective second region. modeling (100, 600, 700) problems existing in cells in a first region to obtain N agents, a modeling method and a training method, wherein 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 in the first region 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; or performing network optimization on a cell corresponding to the agent according to the trained initial model of the agent; or determining an initial model of the agent according to the modeling method and the training method, training the initial model of the agent using a reinforcement learning method according to the modeling method and the training method, and performing network optimization on a cell corresponding to the agent according to the trained initial model of the agent; characterized in that, the determination of the first region comprises: acquiring history performance data of all cells in a network; determining, for each of the cells, whether the cell is a normal cell or a problematic cell according to history performance data of the cell; dividing, into a same second region of at least one second region, cells in which existing problems belong to a same problem category among problematic cells; and determining, for each of the at least one second region, the first region according to a geographic location relationship of cells contained in the respective second region and the outline of the respective second region. acquiring history performance data of all cells in a network; determining, for each of the cells, whether the cell is a normal cell or a problematic cell according to history performance data of the cell; dividing, into a same second region of at least one second region, cells in which existing problems belong to a same problem category among problematic cells; and determining, for each of the at least one second region, the first region according to a geographic location relationship of cells contained in the respective second region and the outline of the respective second region.