This study aims to address the critical challenges of energy diffusion， resource conflicts， and high-dimensional action spaces inherent in multi-jammer cooperative jamming within complex electromagnetic environments. Conventional omnidirectional jamming suffers from severe energy inefficiency， while independent decision-making among multiple jammers frequently results in interference overlap. Furthermore， the joint optimization of beam direction， jamming channel， and transmit power creates an exponentially growing action space that traditional reinforcement learning methods struggle to handle. To overcome these limitations， we propose a collaborative decision-making framework based on deep reinforcement learning to achieve three-dimensional joint resource optimization with minimal communication overhead. The proposed method constructs a multi-agent architecture featuring “centralized training with decentralized execution”（CTDE）， where each jammer utilizes an independent deep Q-network to approximate action-value functions based on local observations. Centralized training is achieved through a shared global reward signal defined as the total number of successfully jammed users， aligning individual policies with system-wide objectives without high-bandwidth data exchange. To mitigate Q-value overestimation， double target networks with soft parameter updating are integrated. An adaptive Boltzmann exploration strategy with exponentially decaying temperature is employed to dynamically balance the exploration and the exploitation. The action space is formulated as a three-dimensional joint space integrating beam direction， frequency channel， and power level assignment. Comprehensive simulations conducted in a 400 m×400 m scenario with four communication user pairs and two intelligent jammers demonstrate the effectiveness of the proposed approach. Quantitative results indicate that the jamming success rate reaches approximately 90%， representing a 50% improvement over independent deep reinforcement learning and an 80% improvement over independent Q-learning. This approach effectively resolves resource conflicts in multi-jammer systems through global reward sharing while ensuring low communication overhead. The integration of double target networks and adaptive Boltzmann exploration successfully addresses training instability in high-dimensional spaces. By achieving joint optimization of spatial， spectral， and power resources， the method significantly enhances energy utilization efficiency， providing a robust technical foundation for intelligent electronic countermeasures.Highlights:1. A novel “distributed execution with centralized optimization” multi-agent architecture is proposed to achieve collaborative jamming with minimal communication overhead and exposure to risk.2. An improved deep Q-network algorithm integrating double target networks and adaptive Boltzmann exploration is designed to address Q-value overestimation and balance exploration-exploitation trade-offs.3. A three-dimensional joint optimization framework for beam direction， jamming channel， and transmit power is proposed， and simulation results validate that the proposed method achieves approximately 90% jamming success rate， outperforming independent learning.