Fast deep reinforcement learning anti-jamming algorithm based on similar sample generation
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.
communication anti-jammingdeep reinforcement learningfast anti-jammingreliable communication
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