Intelligent jamming decision-making algorithm based on HRDQN in complex electromagnetic environments

To address the issues of slow convergence speed and poor energy efficiency of existing intelligent jamming decision-making in communication countermeasures scenarios, a hierarchical Rainbow deep Q-network (HRDQN) algorithm was proposed. Firstly, a communication system model subject to non-cooperative intelligent jamming was formulated, and the jamming decision-making process was modeled as a Markov decision process (MDP), deriving the suppression coefficient threshold to quantify jamming effectiveness. Secondly, the action space and decision-making method of the agent were designed to improve decision-making efficiency based on a hierarchical structure. Finally, the reward function was designed to combine the suppression coefficient threshold with the estimated jamming-to-signal ratio (JSR) to guarantee stable convergence of the algorithm. Simulation results demonstrate that the proposed algorithm rapidly generates ideal jamming decisions while reducing power consumption, and outperforms the traditional algorithms in convergence speed, thereby corroborating the merits of the proposed algorithm.