A model parameter learning method in which a learning device learns a model parameter of a movement mode model for determining a movement mode parameter representing a movement mode when a moving device moves from a departure point to a destination under a condition that a plurality of moving objects are present around the moving device, the model parameter learning method performed by the learning device, comprising: a movement mode parameter acquisition step of acquiring a time series of the movement mode parameter of a first moving object when the first moving object moves from the departure point to the destination while avoiding interference with each of a plurality of second moving objects under a condition that the plurality of second moving objects are present around the first moving object; a positional relationship information acquisition step of acquiring a time series of positional relationship information of the plurality of second moving objects in a movement direction of the first moving object when the first moving object moves from the departure point to the destination; an environment information acquisition step of acquiring a time series of environment information which is information indicating an existence state of a stationary object around the first moving object when the first moving object moves from the departure point to the destination; characterized in that the method further comprises: a learning data creation step of creating learning data by associating the time series of the movement mode parameter, the time series of the positional relationship information synchronized with the time series of the movement mode parameter, and the time series of the environment information synchronized with the time series of the movement mode parameter with each other; and a learning step of learning the model parameter of the movement mode model, which outputs the movement mode parameter, by a predetermined machine learning algorithm using the learning data as an input to the movement mode model, wherein in the learning data creation step, the environment information is created as an image having a position of the first moving object as a center on a two-dimensional map, and the image indicates a region in which the stationary object is not present and a region in which the stationary object is present. a movement mode parameter acquisition step of acquiring a time series of the movement mode parameter of a first moving object when the first moving object moves from the departure point to the destination while avoiding interference with each of a plurality of second moving objects under a condition that the plurality of second moving objects are present around the first moving object; a positional relationship information acquisition step of acquiring a time series of positional relationship information of the plurality of second moving objects in a movement direction of the first moving object when the first moving object moves from the departure point to the destination; an environment information acquisition step of acquiring a time series of environment information which is information indicating an existence state of a stationary object around the first moving object when the first moving object moves from the departure point to the destination; characterized in that the method further comprises: a learning data creation step of creating learning data by associating the time series of the movement mode parameter, the time series of the positional relationship information synchronized with the time series of the movement mode parameter, and the time series of the environment information synchronized with the time series of the movement mode parameter with each other; and a learning step of learning the model parameter of the movement mode model, which outputs the movement mode parameter, by a predetermined machine learning algorithm using the learning data as an input to the movement mode model, wherein in the learning data creation step, the environment information is created as an image having a position of the first moving object as a center on a two-dimensional map, and the image indicates a region in which the stationary object is not present and a region in which the stationary object is present. a learning data creation step of creating learning data by associating the time series of the movement mode parameter, the time series of the positional relationship information synchronized with the time series of the movement mode parameter, and the time series of the environment information synchronized with the time series of the movement mode parameter with each other; and a learning step of learning the model parameter of the movement mode model, which outputs the movement mode parameter, by a predetermined machine learning algorithm using the learning data as an input to the movement mode model, wherein in the learning data creation step, the environment information is created as an image having a position of the first moving object as a center on a two-dimensional map, and the image indicates a region in which the stationary object is not present and a region in which the stationary object is present.