A Deep Reinforcement Learning Approach Towards Computation Offloading for Mobile Edge Computing. In order to improve the quality of service for users and reduce the energy consumption of the cloud computing environment, Mobile Edge Computing (MEC) is a promising paradigm by providing computing resources which is close to the end device in physical distance. Nevertheless, the computation offloading policy to satisfy the requirements of the service provider and consumer at the same time within a MEC system still remains challenging. In this paper, we propose an offloading decision policy with threelevel structure for MEC system different from the traditional twolevel architecture to formulate the offloading decision optimization problem by minimizing the total cost of energy consumption and delay time. Because the traditional optimization methods could not solve this dynamic system problem efficiently, Reinforcement Learning (RL) has been used in complex control systems in recent years. We design a deep reinforcement learning (DRL) approach to minimize the total cost by applying deep Qlearning algorithm to address the issues of too large system state dimension. The simulation results show that the proposed algorithm has nearly optimal performance than traditional methods.

When Learning Joins Edge: RealTime Proportional Computation Offloading via Deep Reinforcement Learning Computation offloading makes sense to the interaction between users and computeintensive applications. Current researches focused on deciding locally or remotely executing an application, but ignored the specific offloading proportion of application. A full offloading cannot make the best use of client and server resources. In this paper, we propose an innovative reinforcement learning (RL) method to solve the proportional computation problem. We consider a common offloading scenario with timevariant bandwidth and heterogeneous devices, and the device generates applications constantly. For each application, the client has to choose locally or remotely executing this application, and determines the proportion to be offloaded. We formalize the problem as a longterm optimization problem, and then propose a RLbased algorithm to solve it. The basic idea is to estimate the benefit of posible decisions, of wihch the decision with the maximum benefit is selected. Instead of adopting the original Deep Q Network (DQN), we propose Advanced DQN (ADQN) by adding Priority Buffer Mechanism and Expert Buffer Mechanism, which improves the utilization of samples and overcomes the cold start problem, respectively. The experimental results show ADQNu0027s high feasibility and efficiency compared with several traditional policies, such as None Offloading Policy, Random Offloading Policy, Link Capacity Optimal Policy, and Computing Capability Optimal Policy. At last, we analyse the effect of expert buffer size and learning rate on ADQNu0027s performance.