The machine learning apparatus includes: a state data observing unit which observes state data of the laser apparatus, including data output from a reflected light detecting unit for measuring a reflected light amount; an operation result acquiring unit which acquires a success/failure result indicating whether the machining has been started successfully by the laser beam output from a laser oscillator; a learning unit which learns light output command data by associating the light output command data with the state data of the laser apparatus and the success/failure result of the machining start; and a decision making unit which determines the light output command data by referring to the light output command data learned by the learning unit.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus for learning light output command data containing a light output command to be issued to the laser power supply unit to start the machining, for use in a laser apparatus comprising at least one laser oscillator for performing machining such as cutting or welding by applying laser beam to a workpiece and at least one or more laser power supply units for supplying power to the laser oscillator, the machine learning apparatus comprising: a state data observing unit which observes state data of the laser apparatus, including data output from an output light detecting unit for measuring an output light amount which is the amount of the laser beam output from the laser oscillator by passing through a laser optical system, and data output from a reflected light detecting unit for measuring a reflected light amount which is the amount of reflected light, the reflected light being the laser beam reflected from a surface of the workpiece back into the laser oscillator and/or the laser optical system; an operation result acquiring unit which acquires a success/failure result indicating whether the machining has been started successfully by the laser beam that the laser oscillator output based on the light output command data; a learning unit which receives an output from the state data observing unit and an output from the operation result acquiring unit, and learns the light output command data containing the light output command to the laser power supply unit by associating the light output command data with the state data of the laser apparatus and the success/failure result of the machining start; and a decision making unit which determines the light output command data containing the light output command to the laser power supply by referring to the light output command data learned by the learning unit, and wherein the machine learning apparatus learns the light output command data for starting the machining on the workpiece within a predefined time period while satisfying a condition that the reflected light amount does not exceed a second predetermined level which is set higher than a first predetermined level. 2. The machine learning apparatus according to claim 1, wherein when the reflected light amount obtained from the output data of the reflected light detecting unit has reached the first predetermined level, a command for temporarily shutting off or reducing the laser beam output is immediately issued to the laser power supply unit even when the laser power supply unit is in the process of executing the light output command, and the decision making unit re-outputs new next light output command data. 3. The machine learning apparatus according to claim 1, wherein the second predetermined level for the reflected light amount is set lower than an alarm level corresponding to a light amount above which the reflected light amount is highly likely to damage the laser oscillator and/or the laser optical system. 4. The machine learning apparatus according to claim 1, wherein the decision making unit outputs the light output command data by determining the light output command data substantially in succession in a time series manner, at least during the predefined time period, until succeeding in starting the machining. 5. The machine learning apparatus according to claim 1, wherein when determining the light output command data, if there is any previous light output command data output before that light output command data, the output data of the reflected light detecting unit obtained for the laser beam output based on the previous light output command data is utilized in determining the light output command data. 6. The machine learning apparatus according to claim 1, wherein the operation result acquiring unit utilizes the output data of the reflected light detecting unit. 7. The machine learning apparatus according to claim 1, wherein the success/failure result of the machining start to be acquired by the operation result acquiring unit is given in the form of a result indicating whether or not the light output command data that substantially coincides with the light output command data necessary or suitable for the machining of the workpiece has been successfully output within the predefined time period. 8. The machine learning apparatus according to claim 1, wherein the operation result acquiring unit acquires at least one of results which include the success or failure of the machining start, the time required to start the machining, the number of times that, or the frequency with which, the reflected light amount reached the first predetermined level, and an amount corresponding to a time-integrated value of an amount by which the reflected light amount exceeded the first predetermined value. 9. The machine learning apparatus according to claim 1, wherein the learning unit includes a reward calculating unit for calculating a reward based on an output of the operation result acquiring unit, and a value function that determines a value of the light output command data, and further includes a value function updating unit for updating the value function based on the reward. 10. The machine learning apparatus according to claim 1, wherein the learning unit includes a learning model for learning the light output command data, and further includes an error calculating unit for calculating an error based on an output of the operation result acquiring unit and on the learning model, and a learning model updating unit for updating the learning model based on the error. 11. The machine learning apparatus according to claim 1, wherein the machine learning apparatus includes a neural network. 12. The machine learning apparatus according to claim 1, wherein the state data input to the state data observing unit and/or the light output command data output from the decision making unit include at least one of the following: laser beam output, peak output, pulse width, pulse spacing, repetition frequency, number of repetitive pulses, assist gas spraying conditions, transmittance, focal length, and F value of the laser optical system, spot size of the laser beam focused on the surface of the workpiece, relative positional relationship between the surface of the workpiece and the focused point of the laser beam, incident angle of the laser beam relative to the surface of the workpiece, temperature of the workpiece, ambient temperature, and ambient humidity. 13. A laser apparatus equipped with the machine learning apparatus of claim 1, comprising: the laser oscillator; the laser power supply unit; the laser optical system; the output light detecting unit; the reflected light detecting unit; and a control apparatus which controls the laser oscillator, the laser power supply unit, the laser optical system, the output light detecting unit, and the reflected light detecting unit independently of each other. 14. The laser apparatus according to claim 13, wherein the laser apparatus includes at least two or more reflected light detecting units, and at least one reflected light detecting unit for detecting each of a plurality of reflected lights propagating through different light paths. 15. The laser apparatus according to claim 13, wherein the laser apparatus includes a light source different from the laser oscillator, a light detector for detecting light from the light source, and an optical system through which the light from the light source is applied to the workpiece and through which the light reflected from the surface of the workpiece is incident on the light detector, so that a state of the light reflected from the workpiece can be observed even when the laser beam from the laser oscillator is not output, and a result of the observation is input as one of the state data to the state data observing unit. 16. The laser apparatus according to claim 13, wherein the laser apparatus includes an infrared detecting unit and/or a plasma light detecting unit for detecting infrared and/or plasma light radiated from near a designated position on the workpiece where the laser beam is applied or is intended to be applied, and data acquired by the infrared detecting unit and/or the plasma light detecting unit is input as one of the state data to the state data observing unit. 17. The laser apparatus according to claim 13, wherein there exist a plurality of the laser apparatus, and a plurality of the machine learning apparatus, which are provided one for each of the laser apparatus, are configured to share or exchange data with each other via communication media. 18. The laser apparatus according to claim 17, wherein the machine learning apparatus resides in a cloud server. 19. A machine learning method for learning light output command data which contains a light output command to the laser power supply unit for starting the machining of the workpiece by the laser beam within the predefined time period while satisfying the condition that the reflected light amount does not exceed a second predetermined level, the machine learning method comprising: observing state data of the laser apparatus, including the data output from the output light detecting unit for measuring the output light amount and the data output from the reflected light detecting unit for measuring the reflected light amount; acquiring a success/failure result indicating whether the machining has been started successfully by the laser beam; receiving the observed state data of the laser apparatus and the acquired success/failure result of the machining start effected by the laser beam; and learning the light output command data containing the light output command to the laser power supply unit by associating the light output command data with the state data of the laser apparatus and the success/failure result of the machining start. 20. A machine learning method for learning light output command data which contains a light output command to the laser power supply unit for starting the machining of the workpiece by the laser beam within the predefined time period while satisfying the condition that the reflected light amount does not exceed a second predetermined level, the machine learning method comprising: observing state data of the laser apparatus, including the data output from the output light detecting unit for measuring the output light amount and the data output from the reflected light detecting unit for measuring the reflected light amount; acquiring a result for the light output command data, the result including the reflected light amount detected from the workpiece; and learning the light output command data containing the light output command to the laser power supply unit by associating the light output command data with the state data of the laser apparatus and the result of the light output command data including the reflected light amount.