Markov decision process with deep reinforcement learning for robotics data offloading in cloud network

Abstract

Robots have a wide range of computer capacities, and executing powerful computational programs on them might be difficult due to restricted internal processing, memory, and energy. Similarly, cloud computing enables on-demand computation, so integrating robotics and cloud computing can help robots solve limitations. The key to successfully offloading jobs is an operational solution that would not underutilize the robot’s natural processing capacity and act authorized based on important costing criteria like delay and CPU resources. Applications are offloaded from robots based on the Markovian decision process. The Markovian decision helps to identify the resources in the cloud network based on probability. A deep reinforcement learning-based deep Q-network (DQN) technique selects resources in the cloud network. Further data are offloaded in cloud storage. The state-space is built on the notion that the size of the input information has a serious influence on the software’s processing time. The suggested technique is constructed as a repetitive work issue with a distinct space domain, in which we take a different activity at every successive stage using the resulting result to train the DQN to get the most prizes. A navigation testbed was created and implemented to validate the suggested method. The proposed method minimizes the cost of communications between clouds and also it minimizes the latency of the application. It increases the accuracy level by 85%, which is higher when compared with existing methods.