DRAM:A DRL-based resource allocation scheme for MAR in MEC

Mobile Edge Computing(MEC)and 5G technology allow clients to access computing resources at the network frontier,which paves the way for applying Mobile Augmented Reality(MAR)applications.Under the MEC paradigm,MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real environment.However,the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network frontier.In this paper,we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into consideration.We first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning paradigm.Then,we propose DRAM,a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in MEC.We also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation problems.The simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’adaptation algorithms.

Automated Penetration Testing with Fine-Grained Control through Deep Reinforcement Learning

Penetration testing(PT)is an active method of evaluating the security of a network by simulating various types of cyber attacks in order to identify and exploit vulnerabilities.Traditional PT involves a time-consuming and labor-intensive process that is prone to errors and cannot be easily formulated.Researchers have been investigating the potential of deep reinforcement learning(DRL)to develop automated PT(APT)tools.However,using DRL in APT is challenged by partial observability of the environment and the intractability problem of the huge action space.This paper introduces RLAPT,a novel DRL approach that directly overcomes these challenges and enables intelligent automation of the PT process with precise control.The proposed method exhibits superior efficiency,stability,and scalability in finding the optimal attacking policy on the simulated experiment scenario.