Conditional diffusion-enhanced deep reinforcement learning for data-efficient cognitive interference

Chuang Peng

doi:10.1016/j.phycom.2026.103098

2026-07-01

PRE

DOI：10.1016/j.phycom.2026.103098

原文链接

摘要

En 中文

深度强化学习（DRL）在自适应干扰决策中展现出强大潜力，能够使智能体动态调整策略以应对时变通信环境。然而，基于DRL的方法通常因在线交互机会有限和数据获取成本高而导致样本效率低下，这显著制约了其实际部署。为解决此挑战，本文提出了一种条件去噪扩散概率模型（CDDPM）增强的DRL框架，通过经验数据增强提高学习效率。所提方法将DRL经验元组重新表述为与扩散兼容的训练样本，并利用CDDPM合成状态-动作一致的经验数据，从而减少所需的在线环境交互次数。具体而言，设计了一种数据转换策略，从现有DRL经验构建扩散训练集和条件输入，实现生成未见但行为一致的数据。为确保证在线学习初期的有效数据合成，CDDPM使用离线DRL经验数据进行预训练。此外，引入包含状态和动作变量的条件信息以引导扩散过程，在生成多样性与保真度之间实现平衡。实验结果表明，所提框架显著加速了DRL收敛，并在学习效率和干扰性能方面优于传统DRL及现有生成式数据增强方法。

Keyword:

Cognitive interference strategy

Deep reinforcement learning

Experience data synthesis

Conditional denoising diffusion probabilistic model

期刊

Physical Communication

IF:

2.2

论文数:

363

被引数:

2.6K

机构

national university of defense technology - china

学者数:

1.8W

论文数: 1.4W

被引数: 9

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