Offline Reward Backfilling-Based Intelligent Cooperative Jamming Strategy Learning Method

To address partial observability, reward sparsity, and distorted credit assignment in cooperative jamming against networked radars in complex electromagnetic environments, this paper proposes an intelligent cooperative jamming strategy learning method with an offline reward backfilling mechanism. The multi-jammer cooperative jamming process is formulated as a partially observable Markov decision process. A two-level reward design is introduced, integrating immediate rewards with offline reward backfilling to improve the evaluation of jamming effectiveness and policy learning. Specifically, within each aggregation period, each jammer performs online adaptations of interception rhythm and transmission behavior based on local observations. At the end of the period, multijammer interaction data are aggregated to retrospectively assess the effectiveness of joint jamming actions. The resulting evaluation is backfilled into policy optimization to refine the policy gradient signal. This design enhances the policy’s ability to capture the actual jamming effectiveness. Accordingly, under the centralized training and decentralized execution framework, an offline reward backfilling-based multi-agent proximal policy optimization algorithm, termed ORB-MAPPO, is developed to realize collaborative time–frequency jamming strategy learning for multiple jammers. Simulation results demonstrate that the proposed method stably learns effective time–frequency cooperative jamming strategies, achieving a jamming coverage rate of over 95% and an information interception rate close to 100%. Compared with typical multi-agent policy optimization methods, the proposed method improves the jamming coverage rate by approximately 20%, demonstrating superior cooperative jamming performance and training stability.