| 标题 |
 Deep Reinforcement Learning and Markov Decision Problem for Task Offloading in Mobile Edge Computing 移动边缘计算中任务卸载的深度强化学习和马尔可夫决策问题
相关领域
计算机科学
强化学习
移动边缘计算
马尔可夫决策过程
可扩展性
边缘计算
分布式计算
人工智能
GSM演进的增强数据速率
机器学习
马尔可夫过程
统计
数学
数据库
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| 其它 | Mobile Edge Computing (MEC) offers cloud-like capabilities to mobile users, making it an up-and-coming method for advancing the Internet of Things (IoT). However, current approaches are limited by various factors such as network latency, bandwidth, energy consumption, task characteristics, and edge server overload. To address these limitations, this research propose a novel approach that integrates Deep Reinforcement Learning (DRL) with Deep Deterministic Policy Gradient (DDPG) and Markov Decision Problem for task offloading in MEC. Among DRL algorithms, the ITODDPG algorithm based on the DDPG algorithm and MDP is a popular choice for task offloading in MEC. Firstly, the ITODDPG algorithm formulates the task offloading problem in MEC as an MDP, which enables the agent to learn a policy that maximizes the expected cumulative reward. Secondly, ITO |
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