A method, implemented on a processing system, for achieving a desired finishing of a surface in a coldspray application and/or a refurbishing process, the method comprising: receiving a plurality of observations of an environment of a coldspray application system and/or a component refurbishing system at a control system; applying an action to the environment in accordance with a control policy of the control system, wherein the action controls a nozzle in the coldspray application and/or the refurbishing process and the observations determine a deposit characteristic on the surface; wherein the desired finishing is a flat surface and the action controls a speed and an angle of the nozzle over multiple applications of material in layers, wherein the action is in accordance with a determined control sequence such that a final material profile matches a prescribed profile, taking into account a weighted sum of a time to complete a pass and a penalty on angular rate; wherein the control policy is developed in a simulation environment by: receiving a plurality of observations of a simulated version of the environment; applying machine learning using deep reinforcement learning to determine an action in the simulated version of the environment based on the observations, wherein the deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations; applying the action to the simulated version of the environment; receiving a reward token indicative of alignment between the action and a desired finishing of a surface in the simulated version of the environment; updating a policy parameter of the control system based on the reward token; and applying the updated policy parameter to determine a subsequent action responsive to a subsequent observation. receiving a plurality of observations of an environment of a coldspray application system and/or a component refurbishing system at a control system; applying an action to the environment in accordance with a control policy of the control system, wherein the action controls a nozzle in the coldspray application and/or the refurbishing process and the observations determine a deposit characteristic on the surface; wherein the desired finishing is a flat surface and the action controls a speed and an angle of the nozzle over multiple applications of material in layers, wherein the action is in accordance with a determined control sequence such that a final material profile matches a prescribed profile, taking into account a weighted sum of a time to complete a pass and a penalty on angular rate; wherein the control policy is developed in a simulation environment by: receiving a plurality of observations of a simulated version of the environment; applying machine learning using deep reinforcement learning to determine an action in the simulated version of the environment based on the observations, wherein the deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations; applying the action to the simulated version of the environment; receiving a reward token indicative of alignment between the action and a desired finishing of a surface in the simulated version of the environment; updating a policy parameter of the control system based on the reward token; and applying the updated policy parameter to determine a subsequent action responsive to a subsequent observation. receiving a plurality of observations of a simulated version of the environment; applying machine learning using deep reinforcement learning to determine an action in the simulated version of the environment based on the observations, wherein the deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations; applying the action to the simulated version of the environment; receiving a reward token indicative of alignment between the action and a desired finishing of a surface in the simulated version of the environment; updating a policy parameter of the control system based on the reward token; and applying the updated policy parameter to determine a subsequent action responsive to a subsequent observation.