Generating fan control signal for computing device

A method for use with a computing device, the method comprising:

• executing an application program;
• while executing the application program, collecting performance data including:
• temperature data received from one or more temperature sensors;
• and
• fan speed data received from a fan tachometer;
• generating a fan control signal at least in part by applying a machine learning model to the performance data, wherein:
• the machine learning model is a reinforcement learning model;
• and
• the machine learning model has a loss function including:
• a fan speed term computed as a function of a fan speed included in the fan speed data;
• and
• a temperature overshoot term added to the loss function when the temperature data indicates a temperature above a predetermined temperature threshold;
• and
• controlling a fan according to the fan control signal.

A computing device comprising:

• one or more processing devices;
• one or more temperature sensors;
• a fan;
• and
• a fan tachometer;
• wherein the one or more processing devices are configured to:
• receive a reinforcement learning model from a server computing device;
• execute an application program;
• while executing the application program, collect performance data including:
• temperature data received from the one or more temperature sensors;
• and
• fan speed data received from the fan tachometer;
• generate a fan control signal at least in part by applying the reinforcement learning model to the performance data, wherein the reinforcement learning model has a loss function including:
• a fan speed term computed as a function of a fan speed included in the fan speed data;
• and
• a temperature overshoot term added to the loss function when the temperature data indicates a temperature above a predetermined temperature threshold;
• control the fan according to the fan control signal;
• and
• perform additional training on the reinforcement learning model based at least in part on the performance data and the loss function of the reinforcement learning model.