用新强化学习方法实现多用户边缘计算的自主任务卸载
A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
- 基于双延迟DDPG算法设计去中心化卸载策略
- 用户可自主学习最优卸载策略，性能优于传统方法
- 适合移动场景下的多用户边缘计算系统
移动边缘计算（MEC）使设备能将计算密集型任务卸载到附近具备算力的边缘服务器。本文研究如何利用深度强化学习（DRL）在去中心化环境下实现多用户动态任务卸载，构建可扩展且反馈有限的高效MEC系统。尽管深度确定性策略梯度（DDPG）算法可分别学习各用户的卸载与本地执行功率分配策略，但仍存在固有缺陷。为此，本文提出基于双延迟DDPG（Twin Delayed DDPG）的新方法，克服上述不足，并支持移动用户场景。数值结果表明，个体用户可通过该方法自主学习有效卸载策略，且所提方案性能优于传统基于DDPG的功率控制策略。
原文摘要 · Abstract (English)
Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement learning (DRL) algorithms are used in an MEC system to find feasible decentralized dynamic computation offloading strategies, which leads to the construction of an extensible MEC system that operates effectively with finite feedback. Even though the Deep Deterministic Policy Gradient (DDPG) algorithm, subject to their knowledge of the MEC system, can be used to allocate powers of both computation offloading and local execution, to learn a computation offloading policy for each user independently, we realized that this solution still has some inherent weaknesses. Hence, we introduced a new approach for this problem based on the Twin Delayed DDPG algorithm, which enables us to overcome this proneness and investigate cases where mobile users are portable. Numerical results showed that individual users can autonomously learn adequate policies through the proposed approach. Besides, the performance of the suggested solution exceeded the conventional DDPG-based power control strategy.
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