Abstract: Various examples are provided related to high-speed autofocus control. In one example, a method includes obtaining a first image of a target with a camera; adjusting focus of the camera by a specified AF bin step size; obtaining a second image of the target; adjusting focus of the camera by the specified AF bin step size; obtaining a third image of the target; determining an optimal focus using data of the second and third images; and adjusting focus of the camera to the optimal focus. In another example, a method includes generating an input vector comprising a sequence of input-output pairs associated with a piezoelectric controlled motion stage that can position a camera relative to a target; determine a control input for the motion stage using a LSTM backpropagation network trained to minimize a cost function over a defined prediction horizon; and applying the control input to the motion stage.

Abstract: A deep reinforcement learning-based information processing method includes: determining whether a target edge computing server enters an alert state according to a quantity of service requests received by the target edge computing server within a preset time period; when the target edge computing server enters the alert state, obtaining preset system status information from a preset memory library; computing an optimal action value corresponding to the target edge computing server based on a preset deep reinforcement learning model according to the preset system status information and preset strategy information; and generating an action corresponding to the target edge computing server according to the optimal action value, and performing the action on the target edge computing server. A deep reinforcement learning-based information processing apparatus for an edge computing server includes a first determining module, an acquisition module, a first computing module, a first generation module.

Abstract: A deep reinforcement learning-based information processing method includes: determining whether a target edge computing server enters an alert state according to a quantity of service requests received by the target edge computing server within a preset time period; when the target edge computing server enters the alert state, obtaining preset system status information from a preset memory library; computing an optimal action value corresponding to the target edge computing server based on a preset deep reinforcement learning model according to the preset system status information and preset strategy information; and generating an action corresponding to the target edge computing server according to the optimal action value, and performing the action on the target edge computing server. A deep reinforcement learning-based information processing apparatus for an edge computing server includes a first determining module, an acquisition module, a first computing module, a first generation module.