A Multi-agent Deep Reinforcement Learning Framework for Resource Allocation Optimization in Agricultural Heterogeneous Network

With the rapid development of smart terminal devices within the consumer electronics sector, optimizing resources across heterogeneous networks has emerged as a critical challenge for enhancing user experience. This paper proposes the multi-agent deep reinforcement learning-based agricultural resource optimization algorithm (MAD3QN), based on multi-agent deep reinforcement learning, to address core issues such as inefficient resource allocation and unstable communication quality within smart device networks. The algorithm integrates a three-layer federated learning framework with a heterogeneous network resource allocation model. It effectively resolves Q-value overestimation through a Dueling Double DQN structure, while employing a multi-agent coordination mechanism to counter interference challenges in complex dynamic environments. Experimental results demonstrate that under a 50-user scenario, the system achieves a capacity of 199.3 Mbps, representing a 3.4% improvement over the next-best algorithm. In high-interference environments, the quality of service (QoS) satisfaction rate reaches 92.7%, with joint energy-spectrum efficiency significantly outperforming existing methods. This provides an efficient and reliable solution for future resource optimization in agricultural IoT environments, offering broad application prospects and practical value.