Intelligent task scheduling strategy for cloud robot based on parallel reinforcement learning

Abstract

Cloud computing is an emerging computing model that has been developed on the basis of grid computing. Its powerful computing capabilities have evoked great vigour in its integration with various industries. The integration of cloud computing and robotics has created the concept of cloud robots. This paper proposes an intelligent task scheduling strategy for cloud robots based on parallel reinforcement learning. Firstly, the cloud computing platform is used to divide the complex reinforcement learning problem into several sub-problems. Then, the task scheduling strategy is used to assign sub-tasks to the robot to learn and summarise the results. Finally, Cloudsim builds a cloud computing platform for simulation experiments to verify the effectiveness of the proposed method. The experimental results show that the proposed method reduces the time for the robot to learn the whole problem and improves the learning efficiency.