Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration
Hunan Normal University（湖南师范大学）；Hunan University of Commerce（湖南商学院）；Northumbria University（诺森比亚大学）；University of Essex（埃塞克斯大学）；Queen Mary University of London（伦敦玛丽女王大学）
Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang, Cunhua Pan
We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lévy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.
Imitation Learning from Observation with Automatic Discount Scheduling
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；UC Berkeley（加州大学伯克利分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）
Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg (UL)（卢森堡大学跨学科安全、可靠性与信任中心（SnT））；Automation and Robotics Research Group (ARG)（自动化与机器人研究组）；Space Systems Research Group (SpaSys)（空间系统研究组）
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems
Ecole Nationale Supérieure d'Informatique（国家高等信息学院）
Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
School of Computer Science, Hunan University of Technology and Business（湖南工商大学计算机学院）；Xiangjiang Laboratory（湘江实验室）；Hunan Normal University（湖南师范大学）；College of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）
