Data indicative of a plurality of observations of an environment are received at a control system. Machine learning using deep reinforcement learning is applied to determine an action based on the observations. The deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. The action is applied to the environment. A reward token indicative of alignment between the action and a desired result is received. A policy parameter of the control system is updated based on the reward token. The updated policy parameter is applied to determine a subsequent action responsive to a subsequent observation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving data indicative of a plurality of observations of an environment at a control system; applying machine learning using deep reinforcement learning to determine an action based on the observations, wherein the deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward; applying the action to the environment; receiving a reward token indicative of alignment between the action and a desired result; 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. 2. The method of claim 1, wherein the deep reinforcement learning applies a guided policy search. 3. The method of claim 1, wherein the deep reinforcement learning applies a deep Q-network. 4. The method of claim 1, wherein the updating of the policy parameter is performed in a simulation environment to develop control policies to apply to a physical environment. 5. The method of claim 1, wherein the action controls a nozzle in a coldspray application, and the observations determine a deposit characteristic on a surface. 6. The method of claim 1, wherein the action controls a refurbishing process, and the observations determine a deposit characteristic on a surface. 7. The method of claim 1, wherein the action comprises a classification of one or more defects in a structure, and the method further comprises: outputting a visual indication of the classification of the one or more of defects in the structure. 8. The method of claim 1, wherein the action comprises controlling alignment of a plurality of sensors. 9. The method of claim 1, wherein the action comprises controlling movement of at least one elevator cab. 10. The method of claim 1, wherein the training set is configured at least in part based on expert knowledge input. 11. A system comprising: a control system; and a processing system operable to receive data indicative of a plurality of observations of an environment at the control system, apply machine learning using deep reinforcement learning to determine an action based on the observations, apply the action to the environment, receive a reward token indicative of alignment between the action and a desired result, update a policy parameter of the control system based on the reward token, and apply the updated policy parameter to determine a subsequent action responsive to a subsequent observation, wherein the deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. 12. The system of claim 11, wherein the deep reinforcement learning applies a guided policy search. 13. The system of claim 11, wherein the deep reinforcement learning applies a deep Q-network. 14. The system of claim 11, wherein the updating of the policy parameter is performed in a simulation environment to develop control policies to apply to a physical environment. 15. The system of claim 11, wherein the action controls a nozzle in a coldspray application, and the observations determine a deposit characteristic on a surface. 16. The system of claim 11, wherein the action controls a refurbishing process, and the observations determine a deposit characteristic on a surface. 17. The system of claim 11, wherein the action comprises a classification of one or more defects in a structure, the system further comprises a display, and a visual indication of the classification of the one or more of defects in the structure is output to the display. 18. The system of claim 11, wherein the action comprises controlling alignment of a plurality of sensors. 19. The system of claim 11, wherein the action comprises controlling movement of at least one elevator cab. 20. The system of claim 11, wherein the training set is configured at least in part based on expert knowledge input.