A machine learning apparatus according to one embodiment of the present invention is a machine learning apparatus for learning a condition associated with adjustment of a current gain parameter in electrical machine control, and comprises: a state observing unit which acquires actual current as well as an integral gain function and a proportional gain function in a current control loop, and observes state variables which include the integral gain function, the proportional gain function, and at least one of an amount of overshoot, an amount of undershoot, and a rise time of the actual current occurring in response to a step-like torque command; and a learning unit which learns the condition associated with the adjustment of the current gain parameter in accordance with a training data set constructed from the state variables.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus for learning a condition associated with adjustment of a current gain parameter in electrical machine control, comprising: a state observing unit configured to acquire actual current as well as an integral gain function and a proportional gain function in a current control loop, and observe state variables which include the integral gain function, the proportional gain function, and at least one of an amount of overshoot, an amount of undershoot, and a rise time of the actual current occurring in response to a step-like torque command; and a learning unit configured to learn the condition associated with the adjustment of the current gain parameter in accordance with a training data set constructed from the state variables. 2. A machine learning apparatus for learning a condition associated with adjustment of a current gain parameter in electrical machine control, comprising: a state observing unit configured to acquire actual current as well as an integral gain function and a proportional gain function in a current control loop, and observe state variables which include the integral gain function, the proportional gain function, and at least one of bandwidth times calculated from an amplitude ratio and an amount of phase delay of the actual current occurring in response to a torque command constructed from a set of sinusoidal waves of different frequencies; and a learning unit configured to learn the condition associated with the adjustment of the current gain parameter in accordance with a training data set constructed from the state variables. 3. The machine learning apparatus according to claim 1, wherein the learning unit is configured to learn the condition in accordance with the training data set which is acquired for a plurality of electrical machines. 4. The machine learning apparatus according to claim 1, further comprising: a reward calculating unit configured to calculate a reward based on the amount of overshoot, the amount of undershoot, and the rise time; and a function updating unit configured to, based on the reward, update a function for adjusting the current gain parameter from a present state variable. 5. The machine learning apparatus according to claim 2, further comprising: a reward calculating unit configured to calculate a reward based on the amplitude ratio and the amount of phase delay; and a function updating unit configured to, based on the reward, update a function for adjusting the current gain parameter from a present state variable. 6. The machine learning apparatus according to claim 4, wherein the reward calculating unit calculates the reward further based on a margin allowed for electric current oscillations. 7. The machine learning apparatus according to claim 4, wherein the reward calculating unit calculates the reward further based on a slope of the integral gain function and a slope of the proportional gain function. 8. The machine learning apparatus according to claim 4, wherein the function updating unit performs reinforcement learning using Q-learning and calculates an initial value of an action value in Q-learning from an inductance value and a winding resistance value of a motor. 9. The machine learning apparatus according to claim 4, wherein the function updating unit performs reinforcement learning using Q-learning and uses deep learning to update an action value in Q-learning. 10. An electrical machine control apparatus comprising the machine learning apparatus according to claim 1 and a motor controller, wherein the motor controller includes a torque command input unit which inputs a torque command, and the machine learning apparatus includes a decision making unit which makes a decision to alter the integral gain function and the proportional gain function in response to an input of a present state variable, based on a result of the learning that the learning unit performed in accordance with the training data set. 11. The electrical machine control apparatus according to claim 10, wherein the learning unit is configured to perform an update by relearning the condition in accordance with an additional training data set constructed from a combination of the present state variable, the altered integral gain function, and the altered proportional gain function. 12. The electrical machine control apparatus according to claim 10, wherein the machine learning apparatus is connected to the motor controller via a network, and the state observing unit is configured to acquire the present state variable via the network. 13. The electrical machine control apparatus according to claim 12, wherein the machine learning apparatus resides in a cloud server. 14. The electrical machine control apparatus according to claim 10, wherein the machine learning apparatus is incorporated in the motor controller. 15. A machine learning method for learning a condition associated with adjustment of a current gain parameter in electrical machine control, comprising: acquiring actual current as well as an integral gain function and a proportional gain function in a current control loop, and observing state variables which include the integral gain function, the proportional gain function, and at least one of an amount of overshoot, an amount of undershoot, and a rise time of the actual current occurring in response to a step-like torque command; and learning the condition associated with the adjustment of the current gain parameter in accordance with a training data set constructed from the state variables. 16. A machine learning method for learning a condition associated with adjustment of a current gain parameter in electrical machine control, comprising: acquiring actual current as well as an integral gain function and a proportional gain function in a current control loop, and observing state variables which include the integral gain function, the proportional gain function, and at least one of bandwidth times calculated from an amplitude ratio and an amount of phase delay of the actual current occurring in response to a torque command constructed from a set of sinusoidal waves of different frequencies; and learning the condition associated with the adjustment of the current gain parameter in accordance with a training data set constructed from the state variables.