A Reinforcement Learning-Based Resource Allocation Scheme for Cloud Robotics

• Main Authors: Hang Liu, Shiwen Liu, Kan Zheng
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Cloud robotics; reinforcement learning; resource allocation
• Online Access: https://ieeexplore.ieee.org/document/8314091/

In recent years, robotic systems combined with cloud computing capability have become an emerging topic of discussion in academic fields. The concept of cloud robotics allows the system to offload computing-intensive tasks from the robots to the cloud. An appropriate resource allocation scheme is necessary for the cloud computing service platform to efficiently allocate its computing resources, when the robots send requests asking for computing service. This paper proposes a resource allocation scheme based on reinforcement learning (RL), which can make the cloud to decide whether a request should be accepted and how many resources are supposed to be allocated. The scheme realizes an autonomous management of computing resources through online learning, reduces human participation in scheme planning, and improves the overall utility of the system in the long run. Numerical results demonstrate that the proposed RL-based computing resource allocation scheme has better performances than the greedy allocation scheme.