A robotic device is disclose as having deep reinforcement learning capability. The device includes non-transitory memory comprising instructions and one or more processors in communication with the memory. The instructions cause the one or more processors to receive a sensing frame, from a sensor, comprising an image. The processors then determine a movement transition based on the sensing frame and the deep reinforcement learning, wherein the deep reinforcement learning uses at least one of a map coverage reward, a map quality reward, or a traversability reward to determine the movement transition. The processors then update an area map based on the sensing frame and the deep reinforcement learning using a visual simultaneous localization and mapping (SLAM) process to determine the map updates.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A robotic device having deep reinforcement learning capability, the device comprising: non-transitory memory comprising instructions; and one or more processors in communication with the memory, wherein the one or more processors execute the instructions to: receive a sensing frame, from a sensor, comprising an image; determine a movement transition based on the sensing frame and the deep reinforcement learning, wherein the deep reinforcement learning uses at least one of a map coverage reward, a map quality reward, or a traversability reward to determine the movement transition; and update an area map based on the sensing frame and the deep reinforcement learning using a visual simultaneous localization and mapping (SLAM) process to determine the map updates. 2. The robotic device of claim 1, wherein the sensor comprises at least one of an image sensor or a depth sensor. 3. The robotic device of claim 1, wherein the sensing frame comprises a plurality of pixels and the one or more processors execute instructions to concatenate values of each of the plurality of pixels to generate a sensing input to a deep Q-network, wherein the deep Q-network is configured to generate estimated Q-values based on the sensing input. 4. The robotic device of claim 3, wherein the one or more processors execute instructions to select a robotic device action based on the estimated Q-values. 5. The robotic device of claim 4, wherein the one or more processors execute instructions to generate a second sensing frame from the sensor subsequent to the sensing frame. 6. The robotic device of claim 5, wherein the one or more processors execute instructions to determine a robotic camera location in a map environment based on the second sensing frame. 7. The robotic device of claim 6, wherein the one or more processors execute instructions to determine the robotic camera location by using a particle filtering method or extracting salient points from the sensing frame and tracking the salient points in the second sensing frame. 8. The robotic device of claim 6, wherein the one or more processors execute instructions to use the map updates to determine the robotic camera location. 9. The robotic device of claim 1, wherein the one or more processors execute instructions for the deep reinforcement learning to determine a total reward based on a sum of the map coverage reward, the map quality reward, and the traversability reward to determine the movement transition, wherein each of the map coverage reward, map quality reward, and traversability reward comprises a weighting factor. 10. A computer-implemented method for mapping with deep reinforcement learning in a robotic device, the method comprising: receiving a sensing frame, from one or more sensors, comprising an image; determining robotic device movement based on the sensing frame and the deep reinforcement learning, wherein the deep reinforcement learning uses a plurality of rewards to determine a selected action for the robotic device movement; and updating an area map based on the sensing frame and the deep reinforcement learning using a visual simultaneous localization and mapping (SLAM) process to determine the map updates. 11. The computer-implemented method of claim 10, further comprising: determining a subsequent sensing frame to the sensing frame; determining a total reward based on a sum of a map coverage reward, a map quality reward, and a traversability reward; and determining a transition set comprising a transition generated from the sensing frame, a transition generated from the subsequent sensing frame, the selected action, and the total reward. 12. The computer-implemented method of claim 11, further comprising randomly sampling at least one transition set from a plurality of transition sets to generate a target loss. 13. The computer-implemented method of claim 12, further comprising updating a deep Q-network based on the target loss. 14. The computer-implemented method of claim 11, wherein the selected action is determined based on a total reward rt=αrc+βrq+γrv, wherein rc is the map coverage reward, rq is the map quality reward, rv is the traversability reward, and α, β, γ are reward weights. 15. A non-transitory computer-readable media storing computer instructions for visual simultaneous localization and mapping with deep reinforcement learning in a robotic device, that when executed by one or more processors, cause the one or more processors to perform the steps of: receive a sensing frame, from a sensor, comprising an image; determine robotic device direction of movement based on the sensing frame and the deep reinforcement learning, wherein the deep reinforcement learning uses a total reward comprising a map coverage reward, a map quality reward, and a traversability reward to determine the robotic device movement; and update an area map based on the sensing frame and the deep reinforcement learning using a visual simultaneous localization and mapping (SLAM) process to determine map updates. 16. The non-transitory computer-readable media of claim 15, wherein the computer instructions further cause the one or more processors to: concatenate values of each of a plurality of pixels of the sensing frame to generate a sensing input to a deep Q-network; and generate estimated Q-values for possible actions of the robotic device based on the sensing input, wherein the possible actions are based on discretization of possible moving directions in a coordinate system representing an environment map. 17. The non-transitory computer-readable media of claim 16, wherein the computer instructions further cause the one or more processors to locate the sensor by: extraction of salient feature points from the sensing frame; tracking of the salient feature points in a subsequent sensing frame by matching the salient feature points to salient feature points from the subsequent sensing frame; and determining a sensor pose space and location in a three dimensional map. 18. The non-transitory computer-readable media of claim 17, wherein the salient features are represented by a set of descriptors. 19. The non-transitory computer-readable media of claim 18, wherein the set of descriptors comprise Scale-Invariant Feature Transform (SIFT) descriptors, Speeded UP Robust Features (SURF) descriptors, Oriented Features from Accelerated Segment Test (FAST) or Rotated Binary Robust Independent Element Features (BRIEF) (ORB) descriptors. 20. The non-transitory computer-readable media of claim 17, wherein the computer instructions further cause the one or more processors to locate the sensor by input from inertial motion sensors.