A machine learning device that learns, with respect to a partial machining path machined by wire electrical discharge machine that machines a workpiece on the basis of a machining program, a correction for a machining path of the machining at a corner portion of the machining path, the machine learning device comprising: a state observation unit configured to observe, as state variables: correction amount data indicating a correction amount for the machining path of the machining at the corner portion; machining path data indicating a partial machining path of the machining path at the corner portion, wherein the machining path data is acquired as a series of vector data extending from a current position of the wire to points obtained by dividing a portion of the machining path before and after the current position of the wire by a predetermined sampling distance; machining condition data indicating the machining conditions of the machining; and machining environment data relating to the machining environment of the machining; a determination data acquisition unit configured to acquire, as determination data: data indicating a machining accuracy realized when machining is performed at the corner portion on the basis of the machining path corrected by the correction amount, and data indicating a machining time required to perform machining at the corner portion on the basis of the machining path corrected by the correction amount; and a learning unit configured to execute an iterative learning cycle on the basis of the state variables and the determination data to learn: the partial machining path; and the correction amount for the machining path during the machining by gradually recognizing characteristics implying a correlation between the correction amount for the machining path and the partial machining path while advancing the iterative learning cycle; wherein the state variables are acquired from the partial machining path, machining conditions, and environmental conditions of an immediately preceding learning period and the correction for the machining path determined during the immediately preceding learning period. a state observation unit configured to observe, as state variables: correction amount data indicating a correction amount for the machining path of the machining at the corner portion; machining path data indicating a partial machining path of the machining path at the corner portion, wherein the machining path data is acquired as a series of vector data extending from a current position of the wire to points obtained by dividing a portion of the machining path before and after the current position of the wire by a predetermined sampling distance; machining condition data indicating the machining conditions of the machining; and machining environment data relating to the machining environment of the machining; correction amount data indicating a correction amount for the machining path of the machining at the corner portion; machining path data indicating a partial machining path of the machining path at the corner portion, wherein the machining path data is acquired as a series of vector data extending from a current position of the wire to points obtained by dividing a portion of the machining path before and after the current position of the wire by a predetermined sampling distance; machining condition data indicating the machining conditions of the machining; and machining environment data relating to the machining environment of the machining; a determination data acquisition unit configured to acquire, as determination data: data indicating a machining accuracy realized when machining is performed at the corner portion on the basis of the machining path corrected by the correction amount, and data indicating a machining time required to perform machining at the corner portion on the basis of the machining path corrected by the correction amount; and data indicating a machining accuracy realized when machining is performed at the corner portion on the basis of the machining path corrected by the correction amount, and data indicating a machining time required to perform machining at the corner portion on the basis of the machining path corrected by the correction amount; and a learning unit configured to execute an iterative learning cycle on the basis of the state variables and the determination data to learn: the partial machining path; and the correction amount for the machining path during the machining by gradually recognizing characteristics implying a correlation between the correction amount for the machining path and the partial machining path while advancing the iterative learning cycle; wherein the state variables are acquired from the partial machining path, machining conditions, and environmental conditions of an immediately preceding learning period and the correction for the machining path determined during the immediately preceding learning period. the partial machining path; and the correction amount for the machining path during the machining by gradually recognizing characteristics implying a correlation between the correction amount for the machining path and the partial machining path while advancing the iterative learning cycle; wherein the state variables are acquired from the partial machining path, machining conditions, and environmental conditions of an immediately preceding learning period and the correction for the machining path determined during the immediately preceding learning period.