Wei Fang^{1, 2, 3, ∗}, Lin Pang¹, Weinan Yi¹

Journal on Artificial Intelligence, Vol.2, No.1, pp. 39-58, 2020, DOI:10.32604/jai.2020.09789 - 15 July 2020

Abstract In recent years, with the rapid development of human society, more and more complex tasks have emerged that require deep learning to automatically extract abstract feature representations from a large amount of data, and use reinforcement learning to learn the best strategy to complete the task. Through the combination of deep learning and reinforcement learning, end-to-end input and output can be achieved, and substantial breakthroughs have been made in many planning and decision-making systems with infinite states, such as games, in particular, AlphaGo, robotics, natural language processing, dialogue systems, machine translation, and computer vision. In More >

Ningcheng Yuan¹, Chao Jia^{2, *}, Jizhao Lu³, Shaoyong Guo¹, Wencui Li³, Xuesong Qiu¹, Lei Shi⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1657-1671, 2020, DOI:10.32604/cmc.2020.09840 - 30 June 2020

Abstract Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight, flexible, isolated and highly portable properties. Cloud services are often instantiated as clusters of interconnected containers. Due to the stochastic service arrival and complicated cloud environment, it is challenging to achieve an optimal container placement (CP) scheme. We propose to leverage Deep Reinforcement Learning (DRL) for solving CP problem, which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge. However, applying DRL method directly dose… More >

Yifei Wei^1,*, Zhaoying Wang¹, Da Guo¹, F. Richard Yu²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 89-104, 2019, DOI:10.32604/cmc.2019.04836

Abstract To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile users’ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user… More >