Joint Optimal Allocation of Resources for Multiple Jammer Based on Multi-Agent Deep Reinforcement Learning

Abstract

In response to the complex scenario where multiple jammers navigate through a netted radar system (NRS), this study presents an optimised allocation algorithm for cooperative jamming resources, namely the Multi-Agent Jamming Resource Allocation (MJCJRA) algorithm, which is based on multi-agent deep reinforcement learning. Initially, the research develops a target fusion detection probability function and a global performance index optimisation function, which are tailored to the specific jamming and radar detection models of the scenario. Subsequently, the multiple jammers are mapped into a multi-agent system with a greedy strategy employed to generate targeted rewards for the jamming agents, enhancing their learning efficiency and performance. The study culminates in the design of evaluation and mixed-strategy networks for the jamming agents. It utilises an exponential mean shift method for soft updates of the target network, adopts priority experience replay and importance sampling methods, and incorporates reward centring into the loss function for network updates. Experimental findings demonstrate that MJCJRA algorithm significantly surpasses the baseline method, the particle swarm optimisation (PSO), the snow ablation optimiser (SAO), the multi-agent deep deterministic policy gradient (MADDPG) and multi-agent proximal policy optimisation (MAPPO), effectively diminishing the detection capability of NRS.