通信学报2024，Vol.45Issue(7)：117-126,10.DOI:10.11959/j.issn.1000-436x.2024131
基于相似性样本生成的深度强化学习快速抗干扰算法
Fast deep reinforcement learning anti-jamming algorithm based on similar sample generation
摘要
Abstract
To improve the learning efficiency of anti-jamming algorithms based on deep reinforcement learning and en-able them to adapt more quickly to unknown jamming environments,a fast deep reinforcement learning anti-jamming al-gorithm based on similar sample generation was proposed.By combining the similarity measurement of state-action pairs,derived from bisimulation,with an anti-jamming algorithm grounded in the deep Q-network,this algorithm was able to quickly learn effective multi-domain anti-jamming strategies in unknown,dynamic jamming environments.Spe-cifically,once a transmission action was completed,the proposed algorithm first interacted with the environment using the deep Q-network to acquire actual state-action pairs.Then it generated a set of similar state-action pairs based on bi-simulation,employing these similar state-action pairs to produce simulated training samples.Through these operations,the algorithm was able to acquire a large number of training samples at each iteration step,thereby significantly accelerat-ing the training process and convergence speed.Simulation results show that under comb sweep jamming and intelligent blocking jamming,the proposed algorithm exhibits rapid convergence speed,and its normalized throughput after conver-gence significantly superior to the conventional deep Q-network algorithm,the Q-learning algorithm,and the improved Q-learning algorithm based on knowledge reuse.关键词
通信抗干扰/深度强化学习/快速抗干扰/可靠通信Key words
communication anti-jamming/deep reinforcement learning/fast anti-jamming/reliable communication分类
信息技术与安全科学引用本文复制引用
周权,牛英滔..基于相似性样本生成的深度强化学习快速抗干扰算法[J].通信学报,2024,45(7):117-126,10.基金项目
国家自然科学基金资助项目(No.62371461) The National Natural Science Foundation of China(No.62371461) （No.62371461）
