Distributed Task Scheduling Algorithm Based on MADDPG in MEC Emergency Networks

In resource-constrained emergency networks, task scheduling algorithms play a crucial role in allocating computational tasks and managing data transmission. Currently, centralized scheduling algorithms face the risk of single-point failures and incur significant overhead in maintaining global computational node states and network topology. Distributed scheduling algorithms typically assume a fixed network topology, making them less adaptable to highly dynamic environments with constantly changing node capabilities and network conditions. To address these challenges, this paper proposes a Multi-agent Deep Deterministic Policy Gradient (MADDPG)-based distributed task scheduling algorithm (MATS). The algorithm deploys an agent on each computational node, which only perceives real-time states of adjacent nodes and links, thereby reducing the overhead of maintaining network states. This paper designs an asymmetric multi-agent structure to accommodate computational nodes with varying performance, introduces a dual-buffer pool structure to accelerate model convergence, and develops an agent action mechanism independent of node scale to enhance adaptability to dynamic network topology changes. Experimental results demonstrate that MATS significantly outperforms existing centralized and distributed approaches in handling node dynamics while achieving task average processing latency comparable to optimal centralized algorithms.