A machine learning device and a machine learning method for optimizing timing at which a tool is to be compensated in a machine tool, and a machine tool including the machine learning device. The machine learning device includes a state observation unit for observing a time interval for compensating the tool, a processing error amount of a workpiece processed by the machine tool, and a machine working ratio of the machine tool as state variables, and a learning unit for learning an action value with respect to a change of a tool compensation interval based on the tool compensation interval, the processing error amount of a workpiece, and the machine working ratio that are observed by the state observation unit.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning device for learning an action value with respect to a change of a tool compensation interval that is a time interval for compensating a tool in a machine tool, the device comprising: a state observation unit for observing the tool compensation interval of the machine tool, a processing error amount of a workpiece processed by the machine tool, and a machine working ratio of the machine tool as state variables; and a learning unit for learning by associating the tool compensation interval, the processing error amount, and the machine working ratio that are observed by the state observation unit with a change amount of the tool compensation interval. 2. The machine learning device according to claim 1, wherein the learning unit includes: a reward calculation unit for calculating a reward based on the tool compensation interval, the processing error amount, and the machine working ratio, and a function update unit for creating or updating an action value table based on the tool compensation interval, the processing error amount, the machine working ratio, and the reward. 3. The machine learning device according to claim 2, further comprising a decision making unit for determining a change amount of the tool compensation interval based on the action value table. 4. The machine learning device according to claim 2, wherein the reward calculation unit provides a negative reward when the processing error amount increases or the machine working ratio decreases, and provides a positive reward when the processing error amount decreases or the machine working ratio increases. 5. The machine learning device according to claim 1, wherein the learning unit computes state variables observed by the state observation unit using a multilayer structure. 6. The machine learning device according to claim 2, wherein the function update unit updates the action value table thereof using an action value table updated by another learning machine device. 7. A machine tool including the machine learning device according to claim 1. 8. The machine tool according to claim 7, wherein the state observation unit further observes a temperature inside the machine tool, and wherein the learning unit learns by associating the tool compensation interval, the processing error amount, the machine working ratio, and the temperature inside the machine tool that are observed by the state observation unit with a change amount of the tool compensation interval. 9. A machine learning method for learning an action value with respect to a change of a tool compensation interval that is a time interval for compensating a tool in a machine tool, the method comprising: a step of observing the tool compensation interval of the machine tool, a processing error amount of a workpiece processed by the machine tool, and a machine working ratio of the machine tool as state variables; and a step of learning by associating the tool compensation interval, the processing error amount, and the machine working ratio that are observed by the state observation unit with a change amount of the tool compensation interval. 10. The machine learning method according to claim 9, wherein the step of learning includes: a step of calculating a reward based on the tool compensation interval, the processing error amount, and the machine working ratio, and a step of creating or updating an action value table based on the tool compensation interval, the processing error amount, the machine working ratio, and the reward.