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在复杂的云端端场景中,通过生成对抗模拟学习进行DT辅助的资源配置
Xiaoqi Zhang1, Mingyang Xin1, Yuqiong Li2
1Criminal Investigation and Counter-Terrorism College, Criminal Investigation Police University of China, Shenyang, 110035, Liaoning Province, China.
Scientific reports
|February 7, 2026
概括
我们介绍了一种专家驱动的生成对抗模拟学习 (E-GAIL) 模型,用于云端端计算资源配置. 这种方法有效地管理资源,没有事先的知识或实时反,在复杂的场景中表现优于传统方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式计算 (Distributed Computing) 是一种分布式计算.
背景情况:
- 传统的深度强化学习 (DRL) 对于云端计算资源配置,严重依赖于已知的参数和实时奖励.
- 这种依赖在需要动态决策的复杂场景中带来了挑战.
- 当前的方法在有效的服务提供方面扎,当先前的知识或反有限时.
研究的目的:
- 提出一种新的专家驱动的生成对抗模拟学习 (E-GAIL) 模型,用于云端端系统中的联合多资源配置.
- 为了实现有效的资源分配,而不需要先前的状态知识或实时奖励反.
- 在复杂和动态的计算环境中解决传统DRL的局限性.
主要方法:
- 开发了一个DT辅助的E-GAIL模型,利用模仿学习来分配资源.
- 引入了使用Actor-Critic和Noisynet与DT网络历史数据的单专家轨迹生成算法.
- 将多个单个专家轨迹合并为一个多专家轨迹,利用纳什平衡来解决冲突并找到最佳解决方案.
- 通过梯度更新了E-GAIL生成器和区分参数,以匹配多专家轨迹.
主要成果:
- 在任务上传时,E-GAIL代理快速获得资源分配策略,而不依赖于事先的知识或实时奖励.
- 实验结果表明,E-GAIL能够在大规模,杂的环境中实现最适合专家轨迹.
- 拟议的模型有效地处理了多个受限制资源的联合分配.
结论:
- DT辅助的E-GAIL模型为云端端计算中的资源配置提供了强大的解决方案,特别是在复杂和数据稀缺的场景中.
- 通过利用模仿学习和多专家融合,E-GAIL克服了传统DRL的局限性.
- 这种方法提高了动态的大规模计算环境中的决策速度和准确性.
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