A machine learning apparatus that learns laser machining condition data of a laser machining system includes: a state amount observation unit that observes a state amount of the laser machining system; an operation result acquisition unit that acquires a machined result of the laser machining system; a learning unit that receives an output from the state amount observation unit and an output from the operation result acquisition unit, and learns the laser machining condition data in association with the state amount and the machined result of the laser machining system; and a decision-making unit that outputs laser machining condition data by referring to the laser machining condition 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 laser machining condition data of a laser machining system, the laser machining system comprising: at least one laser apparatus that includes at least one laser oscillator; at least one machining head that emits a laser beam from the laser apparatus to a workpiece; at least one output light detection unit that detects an amount of the laser light emitted from the machining head; at least one reflected light detection unit that detects a reflected light emitted from the machining head and reflected on a surface or near the surface of the workpiece to return to the laser apparatus via an optical system in the machining head; at least one machined result observation unit that observes at least one of a machining state and a machined result of the workpiece at least during laser machining or after the laser machining; and at least one driving apparatus that changes a relative positional relationship between the machining head and the workpiece, the machine learning apparatus comprising: a state amount observation unit that observes a state amount of the laser machining system; an operation result acquisition unit that acquires a machined result of the laser machining system; a learning unit that receives an output from the state amount observation unit and an output from the operation result acquisition unit, and learns the laser machining condition data in association with the state amount of the laser machining system and the machined result; and a decision-making unit that outputs laser machining condition data by referring to the laser machining condition data leaned by the learning unit. 2. The machine learning apparatus according to claim 1, wherein when machining of the workpiece based on given laser machining condition data, an amount of the reflected light detected by the reflected light detection unit exceeds a second predetermined level set lower than a first predetermined level set lower than an alarm level indicating that at least one of the machining head, the laser apparatus, and a laser light propagation optical component between the machining head and the laser apparatus may be damaged by the reflected light, the learning unit refers to the learned laser machining condition data and outputs laser machining condition data predicted to be obtained a machined result close to a machined result of the give laser machining condition data without the amount of the reflected light detected by the reflected light detection unit exceeding the second predetermined level. 3. The machine learning apparatus according to claim 1, wherein the state amount observed by the state amount observation unit includes at least one of the followings: optical output characteristics of the laser apparatus indicating a relationship between an optical output command for the laser apparatus and an optical output actually emitted from the laser apparatus; an optical output emitted from the laser apparatus; a ratio of an optical output emitted from the machining head to the optical output from the laser apparatus; a temperature of a portion thermally connected to the laser oscillator; temperatures of portions including a component changed in temperature due to laser oscillation in the laser apparatus; a temperature of the machining head; a temperature of the optical system that propagates the laser light from the laser apparatus to the machining head; a temperature of the driving apparatus; a temperature of a structural component that supports the machining head or the driving apparatus; a kind, a temperature, and a flow rate of fluids for cooling the component increased in temperature due to the laser oscillation; a temperature and humidity of air in the laser apparatus; an environmental temperature and humidity around the laser apparatus; an actual current of a driving motor of the driving apparatus; an output from a position detection unit of the driving apparatus; and sizes including a thickness, material quality, specific heat, a density, heat conductivity, a temperature, and a surface state of the workpiece. 4. The machine learning apparatus according to claim 1, wherein the laser machining condition data output from the decision-making unit includes at least one of the followings: an optical output, an optical output waveform, beam mode, and a laser wavelength of the laser beam emitted from each laser apparatus; a focal distance, an F-value, and a transmittance of the optical system that emits the laser beam; a relative positional relationship including a time change between a focus of the laser beam emitted to the workpiece and a machined surface of the workpiece; a spot size, a power density, and a power density distribution of the laser beam emitted to the workpiece on the machined surface of the workpiece; a relative positional relationship including a time change between the machining head and the workpiece; an angle formed between an optical axis of the laser beam and the machined surface of the workpiece; a machining speed; and a kind and a flow rate or supply pressure of assist gas. 5. The machine learning apparatus according to claim 2, wherein the learning unit includes: a learning model for learning laser machining condition data varied from one laser machining content to another; an error calculation unit that calculates a difference between a laser machined result including a machining speed or time expended for predetermined machining obtained by the operation result acquisition unit and a substantially ideal machined result or a target machined result including a machining speed or time expended for predetermined machining set for each laser machining content; and a learning model update unit that updates the learning model according to the difference. 6. The machine learning apparatus according to claim 5, wherein in the learning unit, the error calculation unit outputs, when the amount of the reflected light detected by the reflected light detection unit exceeds the second predetermined level during the machining of the workpiece based on the given laser machining condition data, a calculation result indicating that a predetermined difference has occurred between the machined results of the laser machining condition data, and the learning model is updated according to the calculation result. 7. The machine learning apparatus according to claim 2, wherein the learning unit has a value function varied from one laser machining content to another and defining a value of laser machining condition data, and further includes: a reward calculation unit that provides, when a difference between a laser machined result including a machining speed or time expended for predetermined machining obtained by the operation result acquisition unit and a substantially ideal machined result or a target machined result including a machining speed or time expended for predetermined machining set for each laser machining content is small, a plus reward according to the difference, and when the difference is large, provides a minus reward according to the difference; and a value function update unit that updates the value function according to the reward. 8. The machine learning apparatus according to claim 7, wherein in the learning unit, the reward calculation unit provides a predetermined minus reward when the amount of the reflected light detected by the reflected light detection unit exceeds the second predetermined level during the machining of the workpiece based on the given laser machining condition, and the value function is updated according to the predetermined minus reward. 9. A laser machining system provided with the machine learning apparatus according to claim 1, comprising: the laser apparatus; the machining head; the output light detection unit; the reflected light detection unit; the machined result observation unit; and the driving apparatus, in which at least one each is provided; and at least one control apparatus that controls the laser apparatus, the machining head, the output light detection unit, the reflected light detection unit, the machined result observation unit, and the driving apparatus. 10. The laser machining system according to claim 9, wherein when the amount of the reflected light detected by the reflected light detection unit exceeds the first predetermined level, the control apparatus cuts off or reduces to a predetermined ratio the optical output from the laser apparatus without waiting for outputting of next laser machining condition data from the machine learning apparatus. 11. The laser machining system according to claim 9, wherein: optical output characteristics of the laser apparatus indicating a relationship between an optical output command from the control apparatus and an optical output actually emitted from the laser apparatus are stored in the laser apparatus or the control apparatus; and the optical output characteristics are measured along a predetermined schedule, and the optical output characteristics of the laser apparatus are updated based on a measurement result. 12. The laser machining system according to claim 9, wherein: at least one of the machined result observation units is any one of a digital two-dimensional imaging apparatus, a CCD measurement microscope, a contact-type surface roughness/shape measurement apparatus, a white light interferometer, a laser microscope, and a non-contact three-dimensional measurement apparatus; and at least one data among smoothness or surface roughness of a laser cut surface, a volume of bead-like deposits formed on front and rear surfaces near a cut portion per unit cut length, a sputter deposition amount per unit area on the rear surface, an oxide color density on the cut surface, cutting size/shape accuracy, and observation result data of perpendicularity of the cut surface is output as the result of the laser machining condition data from the machined result observation unit to the operation result acquisition unit. 13. The laser machining system according to claim 9, wherein: at least one of the machined result observation units is any one of a digital two-dimensional imaging apparatus, a CCD measurement microscope, a laser microscope, a light excitation nondestructive inspection apparatus including a light source and an infrared camera, an ultrasonic flaw detection testing apparatus, an induction heating nondestructive inspection apparatus, a radiation transmission imaging apparatus, and an acoustic emission testing apparatus; and at least one observation result data among cracks in a welded portion, a blowhole, a pinhole, a lack of penetration, an incomplete fusion, undercut/overlap, and humping caused by the laser, is output as the result of the laser machining condition data from the machined result observation unit to the operation result acquisition unit. 14. The laser machining system according to claim 9, wherein a plurality of laser apparatuses is present, and a plurality of machine learning apparatuses installed in the respective laser machining systems share or exchange data with each other via a communication medium. 15. The laser machining system according to claim 14, wherein the machine learning apparatus is shared by the plurality of laser machining systems via the communication medium. 16. A machine learning method for learning laser machining condition data output to a control apparatus of a laser machining system, comprising: observing a state amount of the laser machining system including output data from at least one laser apparatus, output data from at least one machining head, and output data from at least one driving apparatus for changing a relative positional relationship between the machining head and a workpiece; receiving, as results of laser machining condition data, output data from at least one reflected light detection unit that detects a reflected light returning to the laser apparatus, and output data from at least one machined result observation unit that observes at least one of a machining state and a machined result of the workpiece at least during laser machining or after an end of the laser machining; and learning the laser machining condition data in association with the state amount of the laser machining system and the results of the laser machining condition data.